78-1514

A Computer Simulation of Human

Behavior in Building Fires:

Interim Report

Fred I. Stahl

Environmental Design Research Division

Center for Building Technology

National Engineering Laboratory

National Bureau of Standards

Washington, D.C. 20234

March 13, 1978

Issued September 1978

Prepared for the Center for Fire Research, in support of the

HEW-NBS Fire/Life Safety Program, sponsored by:

U.S. Public Health Service

Department of Health, Education and Welfare

Washington, D.C. 20203

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NBSIR 78-1514

A COMPUTER SIMULATION OF HUMAN

BEHAVIOR IN BUILDING FIRES:

INTERIM REPORT

Fred I. Stahl

Environmental Design Research Division

Center for Building Technology

National Engineering Laboratory

National Bureau of Standards

Washington, D.C. 20234

March 13, 1978

Issued September 1978

Prepared for the Center for Fire Research, in support of the

HEW-NBS Fire/Life Safety Program, sponsored by:

U.S. Public Health Service

Department of Health, Education and Welfare

Washington, D.C. 20203

i

U.S. DEPARTMENT OF COMMERCE. Juanita M. Kreps. Secretary

Dr. Sidney Harman. Under Secretary

Jordan J. Baruch, Assistant Secretary for Science and Technology

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director
A
TABLE OF CONTENTS

LIST OF FIGURES iii

LIST OF TABLES iv

LIST OF APPENDICES v

ACKNOWLEDGEMENTS vi

ABSTRACT . vii

1. INTRODUCTION 1

1.1 This Report: Its Function and Use 1 1.2 Objectives 1 1.3 Significance 2

1 .4 Scope 3

1.5 Previous Research on Simulating Human

Spatial Behavior and Responses to Fire:

Literature Review 4

2. TECHNICAL APPROACH 9

2.1 A Markov-Based Model of Human Behavior

in Building Fires.. 9

2.2 Programming and Running the Computer

Simulation 11

3. BEHAVIORAL ATTRIBUTES OF THE COMPUTER PROGRAM 20

3.1 Overview....... 20

3.2 Subroutines Which Simulate Perception

and Information Gathering 21

3.3 Subroutines Which Simulate Information

Processing and Decision Making 25

4. NON-BEHAVIORAL SYSTEM OPERATIONS 35

4.1 Executive Program 35

4.2 Process Updating 35

4.3 Grid Transformations 36

4.4 Reporting Simulation Results. 36

5. DIRECTIONS FOR FURTHER STUDY 43

5.1 The Simulation Exercise as an Identifier

of Literatures and Data Bases..... 43

5.2 The Simulation Exercise as a Generator

of Researchable Questions 48

ii
6. SUMMARY 51

REFERENCES . 52

APPENDICES A-l

LIST OF FIGURES

FIGURE 2.1 Conceptual Diagram Illustrating the Interaction

of Behavioral Subsystems in Markovian Time/Space..... 15

FIGURE 2.2 Relationship Between Subroutines and the Model's

Structure 16

FIGURE 2.3 Event Control by the Executive Program 17

FIGURE 2.4 Subroutine Calling Pattern 19

FIGURE 3.1 Possible vs. Impossible Spatial Movements 34

FIGURE 4.1 (a) Relationship Between "x,y" and "k-Grid"

Spatial Coordinate Systems 38

(b) The "x,y" Coordinate System 39

(c) Physical Barriers Preclude Certain

Move Alternatives 39

(d) Spatial Sensitivity May be Improved... 40

FIGURE 4.2 Output from Subroutine REPORT.. 41

FIGURE 4.3 Output from Subroutine TRACE.... 42

FIGURE 4.4 Output from Subroutine TOTALS 42

FIGURE A . 1 A Single Probability Matrix Describing a Stationary

Markov Process A-7

FIGURE A. 2 A Set of Probability Matrices Describing a Non-stationary Markov Process A-7

FIGURE A. 3 Breaux's Hotel Fire as a Stationary Markov

Process... A

FIGURE A. 4 Nelson's "State-transition" Model of the Building

Fire System A

FIGURE D.l Describing the Floor-plan for the Computer

in X,Y Space D-9

iii
LIST OF FIGURES

FIGURE D.2 Locating Occupants on the Floor-plan. . D-10

LIST OF TABLES

TABLE D.l Input Data Sequence and Format. D-4

TABLE D.2 Sample Input File D-5

TABLE D.3 Glossary of Input Variable Names. D-7

LIST OF APPENDICES

APPENDIX A: A Markovian Analysis of Human Behavior

in Fires A-l

APPENDIX B: A Description of the Markov-Based

Behavioral Model Underlying BFIRES.... B-l

APPENDIX C: FORTRAN-V Listing of the BFIRES

Simulation Program C-l

APPENDIX D: Preparing a BFIRES Input File D-l

iv
ACKNOWLEDGEMENTS

The Environmental Design Research Division, Center for Building Technology, National Bureau of Standards, is developing a more comprehensive

understanding of systems of pedestrian movement within buildings. The

responses of building occupants during fire emergencies is an important

part of this overall effort.

This report is an interim product of the joint effort of the Department

of Health, Education and Welfare and the Center for Fire Research of the

National Bureau of Standards. This joint effort consists of products in

the areas of decision analysis, fire and smoke detection, smoke movement

and control, automatic extinguishment, and the behavior of institutionalized populations in fire situations. This report is an element of the

human behavioral area.

The author wishes to acknowledge the . administrative support of Mr. Harold

Nelson and Dr. Bernard Levin, of the Center for Fire Research. The

author also thanks Drs. Gary Winkel and Susan Saegert of the City University of New York, and Dr. Dane Harwood of the National Bureau of Standards, for their assistance in matters concerning psychological and

behavioral theory. The extremely capable technical assistance of Mr.

Lawrence Kaetzl in matters involving the use of the Center for Building

Technology's INTERDATA 7/32 minicomputer is greatfully acknowledged.

Finally, Drs. Edward Arens and Robert Glass, and Mr. James Harris, all

of the Center for Building Technology, are acknowledged for their critical reviews of drafts of this report.

v
ABSTRACT

This interim report presents the conceptual development , structure, and

function of BFIRES, a computer program designed to simulate human movement

behavior during building fires. The basic model underlying BFIRES is

derived from a non-stationary , discrete time Markov Process, This model

postulates that occupants construct their emergency responses and behavioral decisions dynamically, in response to continually changing social

and environmental information fields. The simulation of this process is

accomplished through BFIRES, a computer program written in FORTRAN-V.

Directions for further study are discussed.

Key Words: Architectural psychology; architectural research; building

fires; computer-aided design; fire computer program; fire research;

fire safety; human performance; model documentation; modeling

technique; programming; simulation.

vi

A COMPUTER SIMULATION OF HUMAN BEHAVIOR IN BUILDING FIRES:

INTERIM REPORT

1. INTRODUCTION

1 . 1 This Report: Its Function and Use

This interim report documents the conceptual development, structure,

and function of BFIRES , a computer program designed to simulate human

movement behavior during building fires. In addition to introducing the

reader to the BFIRES program, the report suggests avenues for future

research.

Although the report is primarily a presentation of the BFIRES program many readers may have very specific interests, and may wish to focus

only on certain sections. The following outline provides a guide to the

report's contents.

Part one is essentially introductory. The objectives, significance,

and scope of the project are described. A review of the most salient

literature on the computer simulation of human spatial and emergency

behavior is provided, in order to place the current effort in an appropriate perspective.

Part two describes the technical approach taken during the course

of this investigation. The question of modeling is considered first,

and a formal argument for the adoption of a particular framework is presented. Second, the specific approach to programming the computer simulation is discussed.

Parts three and four describe the specific structure and content of

the BFIRES simulation program. Part three considers the various human

behavioral attributes of the program. Individual subroutines are presented in full detail, with discussions of their function, background

and rationale, and underlying behavioral assumptions. Part four treats

the non-behavioral subroutines, which are responsible for the overall

system and data-handling operations of BFIRES.

Part five explores directions for further study. First, the simulation exercise is reviewed as a tool which identifies salient literature

and data bases in need of future review or expansion. Second, the exercise is reviewed as a tool for identifying key issues and for generating

researchable questions.

1 .2 Objectives

The project has been guided by the following objectives: (a) to

demonstrate the utility of studying building fires by means of computer

simulation methodology; (b) to present BFIRES, a simulation model of

1
occupant behavior in building fires; (c) to demonstrate the applicability of available literatures to the modeling process, and (d) to expose

hypotheses, inherent in the simulation, for eventual examination.

1 . 3 Significance

Various difficulties associated with available empirical research

on human behavior in fires have been identified by Stahl and Archea

(1977). These include the problems of performing controlled field experiments, the lack of verisimulitude between experimental and actual settings, and questions concerning the validity of post-incident survey

data.

Models which underlie certain empirical findings, (e.g., those con- sidered by Peschl, 1971, and Henderson, 1971) were also reviewed by Stahl

and Archea. Some inadequacies of these models were found by these reviewers to include their apparent focus on the more narrowly defined and

easily measured aspects of the problem, and their failure to account for

some important behavioral phenomena.

The primary significance of the current study is its emphasis on development of a general model of human behavior in fire situations. In

the long term, the value of such a model will be measured on the basis of

its ability to (a) demonstrate and explain dynamic interrelationships

among systemically linked variables, (b) enable the prediction of behavior patterns occuring during fires, and (c) suggest specific areas requiring intensive empirical investigation.

Of even more practical significance in the long-range is the application of valid and reliable simulation models as building design tools.

A vivid example involves the situation in which an architect has ready

access to a computer terminal and video display in his office. By calling the simulation program from a central computer facility, and typing

in various parameters and data which describe occupancy conditions and

emergency scenarios, the architect is enabled to simulate the perfor- mance of his design , and to evaluate it from the standpoint of life

safety criteria. Moreover, he has at his disposal an objective mechanism for selecting a particular design from among some range of alternatives.

Applications such as the one described above require that the

simulation program be "trusted" by practitioners in the field. That is,

its ability to predict realistic emergency outcomes reliably must have

been demonstrated through rigorous empirical efforts. In this context,

I reiterate that the work presented here concerns initial and elementary

steps toward this overall, long-range objective.

2
1.4 Scope

1.4.1 Behavior Patterns

Building regulations concerned with life safety from fire emphasize egress behavior, expressed in terms of the movement of persons from

threatened zones to safe ones. Various assumptions about human spatial

behavior are implied in these regulations. Although many other kinds of

behavior may also be present (e.g., fire-fighting, calling for help, cry

ing), considerations of egress movement have impacted most directly on

the design of buildings. The model developed here also emphasized the

spatial movement behavior of building occupants. Accordingly, we expect

deductions from the model to invite immediate comparison with current

regulatory provisions. For example, how does floor-plan layout influence egress movement during a fire? Are present requirements for the

provision of exits justified, or do occupants 9 emergency behavioral patterns suggest other alternatives? How do factors other than building

design influence emergency egress behavior?

1.4.2 Life Safety Systems

Caravaty and Haviland (1967) were among the first to identify a "chain" of safety-related events, emphasizing the time -dependent nature

of building fire systems. According to these investigators, segments of

the fire incident could be identified such that specific reference to

event categories should be useful in life safety planning and building

design. These event segments, in their assumed sequence of occurrence,

were identified to be (1) detection of the life threat, (2) alerting of

building occupants to the threat, (3) escape and refuge-seeking actions

by occupants, and (4) control and extinguishment of the fire.

Focusing primarily on human behavioral aspects of fire situations.

Nelson (1977) structured a similar sequence of events: (1) discovery,

(2) alarm, (3) reaction, and (4) evacuation. Other investigators, most

notably Wood (1972), Bryan (1977), and Breaux (1977) have, by interviewing fire victims, attempted to elaborate on such structures. "Reaction"

for example, has often been found to include such seemingly diverse

activities as investigation , helping others , and saving possessions , and

not merely exit-seeking.

To the extent that empirical research has actually indicated the

existence of such time-dependent categories of events, various structures suggested by several investigators might usefully be summarized as follows: (1) discovery, or the first indication of human awareness of

the life threat; (2) alerting, or communication of information about the

threat among occupants; (3) decision-making and concomitant action in or on the environment by alerted occupants (which may or may not be adaptive, and which may or may net result in the safe existing of particular

occupants )

3
Assuming this framework, the special focus of the present study is on the third component: decision-making and concomitant action . Accordingly, the remainder of our analysis centers upon cases in which a fire

has in fact been discovered, and that varying amounts of information

about it (e.g., its location) are already known to at least one of the

potentially affected occupants. Our concern shall be, therefore, to

model emergency behavior over time, in response to some given information baseline.

1.4.3 Occupancies

For most practical purposes, the discussion presented here is

relevant to the study of emergency response across a variety of building

types. Our special focus, however, concerns health-care occupancies

such as hospitals and nursing homes. These facilities introduce important constraints to our study, arising from the fact that many occupants

are perceptually, cognitively and physically impaired* and consequently

require some form of assistance at various points during an emergency.

1 . 5 Previous Research on Simulating Human Spatial Behavior and Reponses

to Fire : Literature Review

During the last fifteen years, there has been considerable interest

in the application of computer simulation techniques to the study of

human cognitive behavior (Feldman, 1962; Luce and Raiffa, 1964 ; Simon,

1967, 1969; Apter, 1970; Shultz, 1974). Arguments have frequently concerned the ability to simulate human thinking and decision-making (e.g.,

Neisser, 1963 vs. Simon, 1967), issues of validity, and questions about

what to simulate (e.g., processes or outcomes?). Amidst an often confused philosophical climate, computer programs were written, and many

simulation-based experiments on human cognitive, motor, and social

behavior were conducted (consult the extensive review by Dutton and

Starbuck, 1971).

Moreover, interest in the application of the computer simulations

to the study of micro-scale person-environment relations has also been

evident. Several programs have been written attempting to simulate

pedestrian movement behavior within bounded environments, and there have

also been attempts to consider human behavior in fires through simulation

techniques. Let us review several key examples:

1.5.1 Computer Simulations of Pedestrian Movement Behavior

The chief objective of Krystiniak (1972) was to provide pictorial

computer output demonstrating the effect of floor-plan arrangement on pedestrian circulation patterns in buildings. His "pedestrians" were endowed with the physical characteristics of body dimensions (the so- called body ellipse), with individuality of walking speed, and with the

4
ability to sense obstacles and barriers (e.g., walls). Each pedestrian

was presumed to have one and only one objective in the building: to get

from his randomly selected entrance door to his randomly selected exit

door in the least amount of time. Door selections were made prior to

the actual simulation, and once these initial and terminal points were present, a deterministic distance-minimization routine took over. Pedestrians then negotiated the floor plan, approaching their objectives,

while avoiding collisions with walls or other physical obstacles.

In another example, Studer and Hobson (1973) constructed a model of

spatial movement behavior predicated on an operant discrimination learning base. In moving from point to point in a spatial field, individuals

were seen to continuously select particular routes from among various

available alternatives. When movement along a particular route resulted

in some short-term goal attainment, the probability that the reinforced

individual would make a similar selection decision in the future was increased.

Baer (1974) investigated the simulation of free-flow pedestrian

movement behavior within bounded spaces. In response to difficulties

imposed by models which primarily considered input and output conditions, while ignoring the nature of the behavioral system itself, Baer's

simulation focused on the behavior of the individual pedestrian during

his trip. The model permitted the simultaneous movement of any number

of such individuals, at any level of traffic density, to be simulated.

Within the spatial system, persons were guided with respect to the physical and behavioral environments by a deterministic procedure which

enabled them to alter their speed and direction within any increment of

the journey. The model endowed individual pedestrians with goals,

motives, and the ability to evaluate obstacles. However, it is not

entirely clear how so-called "free flow" behavior is usefully modeled

by a non-stochastic process. As Baer begins to expand the scope of his

model to include patterns of response to distractions from long-range

goals, interpersonal relations, emergency situations, or the psychological effects of crowding, deterministic predictors may not be found to

adequately reflect the spontaneity or uncertainty of actual human

experience

Along somewhat different lines, Lozar (1974) discussed a method for

simulating spatial behavior in an attempt to determine whether a design

influences an individual’s attitudes toward the environment. In his program, the likelihood that a simulated person would proceed along a particular path was determined by observing movement behavior in the real

world (dining hall). Similarly, the likelihood that a simulated person

expressed certain attitudes was derived from correlations between subjective reports and actual behavioral patterns found in the real setting.

1.5.2 Computer Simulations of Human Behavior in Building Fires

Wolpert and Zillmann (1969) made what is perhaps the earliest

attempt to simulate by computer the actions of building occupants during

5
a fire. These investigators constructed a computer model of decisionmaking in a spatial context. Using the theater fire problem as a case

for study, the program largely described the individual’s selection of,

and then movement toward, alternative goals made conspicuous by new information produced by a fire threat that expanded, contracted, or remained stationary. Forced to continuously reassess their location

within a spatial context of uncertainty, actors erected barriers,

advanced, or retreated, working independently or in groups. Selection

from among alternative courses of action depended upon one’s location

within the space, in relation to the positions of the threat and the

various safety zones. This selection process was simulated in a non- stochastic fashion. The Wolpert and Zillmann program operated under the

assumption that, during a highly stressful period, occupants were capable of objectively assessing time and distance values which separate

themselves from both the threat, and from available refuge areas, at any

given moment. Moreover, in considering the theater fire problem in

which all relevant events are occuring within the same spatial field,

it was presumed that occupants were always in full visual command of the

situation. Accordingly, calculated decision making could be considered

possible. The model was capable of generating so-called "panic" within

any individual, which allegedly resulted when the person realized that

attainment of a desired safety goal had become impossible. Individuals

in such a state were then presumed to act mal-adaptively, clogging

impassible exits rather than seeking other egress possibilities.

Concerned with a somewhat different problem context, the simulation

model developed by Edmondo, Hahin and Sinay (1969), and the computer program written by Sinay (1971), featured both stochastic and deterministic

characteristics. Conducting research for the U.S. Navy, these investigators dealt with the problem of ship-board emergencies, such as fires,

bombardments, collisions, etc. The probabilistic components of their

program included establishment of both the time and location of the onset

of the emergency, the initial locations of ship crewmen, elapsed time

until detection of the emergency, and the selection of a response mode

(i.e. , aiding others, securing a space, panicking, escaping). The essential deterministic feature was the selection of an escape route, given

certain environmental conditions, from a small array of pre-determined

possibilities. Accordingly, once an Individual understood the situation, his entire escape route (including possible detours, as necessary)

became known in advance. Prediction of escape time could then be simply

calculated from a knowledge of the route’s length, and a simulated crewman's walking speed.

Most recently, the computer simulation of human behavior in building fires has been considered by Korkemas (1977). For simulated fires

events, his chief objectives were to plot fire spread over time, plot

occupants' movement patterns over time, record the history of occupants'

fates, and to record the history of congestion at building exits. As

with the exercise by Edmondo, et al., Korkemaz ’s simulation program utilized both stochastic and deterministic variables including fire migration, rates of increase of toxic substances in the atmosphere, and occu6
pant movement (i.e., spatial displacement) along predefined paths. As

time advanced, the fire covered ever-wider territory, and the density of

toxic substances in the air increased. These were assumed to have the

major effect of slowing the occupant down, as he moved along his pre-set

path to a refuge zone or exit. The rate of decrease in movement speed was governed by deterministic equations. If occupants failed to reach

their goal before the level of toxicants surpassed their tolerance levels

(adjustable by the experimenter), then they were assumed to have been

consumed by the fire. In commenting upon his work, Korkemas recommended

that future simulation programs would have to account for a "familiarity

factor" (i.e., the notion that some people are more familiar than others

with circulation paths within the building, and that such familiarity

might influence emergency response).

1.5.3 Critique and Summary

Certain limitations to the utility of the simulation programs

reviewed above should be noted. First, each of these assumes an environmental-deterministic basis for behavior. For the fire context, it will

be demonstrated in this report that it is necessary to view the environment in terms of both physical and social components, and to emphasize

the importance of the environment as mediated through perceptual and

cognitive processes. Moreover, this report will stress the importance

of viewing building occupants as active participants , who continually

modify their environment, and are thereby influenced by its changing

structure over time.

In addition, each of the simulations discussed avoided the question

of how particular paths were created, or why they were followed? We

know, of course, that these routes were pre-set, or inserted by the

experimenters, who made certain assumptions about emergency egress. The

difficulties inherent in such assumptions have already been examined

elsewhere (Stahl and Archea, 1977). This report shall stress the importance of simulating occupants' decision-making strategies , and will not

assume that persons mechanically respond to stimuli in their environment.

In summary, a variety of simulation approaches are possible when

studying human spatial behavior and response to building emergencies. In

almost all cases, simulations found in the literature were of the

"behavioral-algorithmic" type, in which theoretical descriptions of behavioral sequences were written into computer programs. To varying degrees,

such programs were found to exhibit combinations of both deterministic and

stochastic attributes. The simulation program presented in the remainder

of this report is a stochastic example of the "behavioral-algorithmic"

type.

No studies found in the literature exemplified a purely "heuristic"

approach, in which the computer would be programmed to simulate human

learning, and would then be required to "learn" its way through a novel

situation. Moreover, no instances of a "non-behavioral algorithmic"

7
approach were found either. Examples of this type of simulation would

include mathematical models of the type generally employed by operations

researchers. Connelly (1977) is currently developing the application of

operations research techniques to the study of human behavior in building

fires

8
2. TECHNICAL APPROACH

2.1 A Markov-Based Model of Human Behavior in Building Fires

Figure 2.1 illustrates an interactive state-transition model

describing the building fire problem. This model shall serve as our working hypothesis.

Simply stated, the model suggests that:

(a) Actions in or on the emergency environment, at any point In

time, have the potential of affecting changes to both its

physical and social attributes;

(b) Actions in or on this environment result directly from one’s

current assessment of available action alternatives;

(c) Such an assessment is contingent upon such factors as:

(1) perceptions of the availability of action alternatives at

t ime t

(2) specific perceptions about the possible outcome of each

alternative

(3) perceptions of information defining the current state of

the emergency environment (i„e., the context for action at

time t);

(4) attitudes toward the emergency event, and toward

"appropriate" behavior at time t;

(5) familiarity with the physical environment, and/or with

formal instructions for behavior during building fires.

It further requires a cognitive analysis of the relative advantages and disadvantages of each action alternative, a process

which results in a "cognitive weighting" of the action possibilities during each point in time.

(d) The emergency environment, as altered by a person’s actions

upon or within it at time t , provides many of the cues, stimuli and information necessary to develope his action decision

at time t+1

Making decisions about action in or on an emergency environment,

then, comprises the keystone of the model. Building fire participants

continuously gather and evaluate information about their environment,

about alternative actions within or upon it, and about the possible consequences of such alternatives. They utilize this information in a process of evaluating and weighing the possible actions, and of deciding

9
what to do - or where to go - next. As the emergency environment undergoes continuous change, perceptions of it change, and as a result, action

strategies and behaviors change.

The decision-making process outlined above is not thought to "determine" action outcomes in the mathematical sense. Rather, "cognitive

weighting" has the effect of biasing behavior in some particular direction. For example, the behavior of a person fleeing the danger zone might be biased toward stopping to help a disabled other if, e.g., there

are no other potential helpers available (from Latane and Barley, 1970).

But the helping action is not necessarily certain, since information

other than the fact that another individual requires help influences

action decision-making (e.g., is there enough time to stop and help, and

still reach safety?).

Depending upon the interactive net of information available to an occupant at time t, we may identify some six generic categories of decision bias. Action at time t may be biased toward: (1) threat evasion

("flight"); (2) exit goal seeking; (3) helping others (to include alert

or alarm); (4) the demand temporarily imposed by a real-time interruption

to goal-seeking behavior (whether cognitive, physiological, or environmental in origin); (5) threat suppression ("fight"); (6) "indecision"

(no net biasing effect; equally strong biases).

A detailed discussion of the Markovian basis of the model is provided in Appendix A. A detailed description of both the structural and

dynamic characteristics of the model, and an indication of assumptions

which underlie it, are presented in Appendix B. For the sake of simplicity, flow diagrams break the model down into a set of "subroutines",

each responsible for a different aspect of perception, decision-making,

and action by persons in a building fire. The model described is general

in scope and purpose (relative to the fire problem), and Appendix A

illustrates its current state of development. In studying the diagrams,

note that the "executive" routine drives the human behavioral sub-system

through a single state-transition (i.e. , between time t and t+1). The

entire sequence must be repeated each time such a state-transition is

made. Also note that whenever a "CALL ..." statement is encountered,

flow of control is routed to the subroutine being called. The results

of that subroutine are then inserted back into the calling routine.

Figure 2.2 indicates the relationship between subroutine names and the

model's structure.

Additional information about the model's dynamics and assumptions

is provided through contingency-trace tables (i.e., "truth tables") also

found in Appendix B. These permit entire scenarios to be constructed and

evaluated, by means of "if . . . then ..." type statements.

10
2.2 Programming and Running the Computer Simulation

2.2.1 Programming the Simulation: BFIRES

BFIRES, the computer program written to simulate human behavior

in building fires, is modular in form. That is, each subroutine has as its purpose some specific function, second, these functions generally

fall into the categories of perception, cognition, and action, relative

to the emergency environment. This modular structure provides us with

a "skeletal” programming approach (Raser, 1969). If we assume that the

most basic elements are present, then additional detail can be added in

the future by either adding modules or enriching (or eliminating) existing ones. Such additions, etc., would be predicated upon research findings from both simulation and real-system investigations. Of course, the

possibility exists that future research will require us to reconsider the

basic organization of the original skeleton.

The various subroutines are linked through an EXECUTIVE, or "main"

program. This routine also satisfies the requirements of reading-in

external data, iterating the simulated cognitive and behavioral processes

for the appropriate numbers of occupants and time-frames (state transitions), and repeating a given simulation experiment any number of times

desired. The coding of the EXECUTIVE, therefore, initializes a simulation experiment, and governs the logical flow of events and decisions as these are suggested by the underlying model. Figure 2.3 provides a simplified flow diagram of the EXECUTIVE. The numbers in the left margin

key the diagram to a brief description of each program step, given below.

Figure 2.4 illustrates the subroutine calling pattern. The complete

FORTRAN listing of the EXECUTIVE program and all subroutines is provided

in Appendix C.

(1) Read input data. (a) Input environmental descriptors : locations of walls and barriers; boundaries of room subdivisions; door information (location; whether manually or automatically closed; whether initially open or closed); exit goal locations available for each spatial

subdivision; location of exits; initial location of fire threat; number

of exits available; number of spatial crowding subdivisions in the floorplan; number of doors in the floor-plan; physical crowding threshhold for

each space. (b) Input occupant descriptors : interruption limit for each

occupant; bystander intervention limit for each occupant; each occupant's

familiarity with emergency exits in the building; initial handicapped/

mobility status for each occupant; each occupant's probability of opening

a closed door; each occupant's probability of closing an open door; initial location of each occupant in the floor-plan. (c) Input simulationrun descriptors : number of replications desired; total length of each

replication (in time-frames); total number of occupants in a given

replication; seed number for random-digit generation.

(2) Iterate the simulation experiment for the desired number of

replications

11
(3) Run each replication for the desired number of time-frames.

(4) Run each occupant through a given time frame.

(5) Identify the spatial sub-division (e.g., room) occupied by

each simulated person.

(6,7) Determine probabilistically whether an occupant's goaldirected behavior is influenced by a cognitive or environmental Interruption, during the time frame. Determine the mode and outcome of any such

interruption.

(8) During the current time-frame, is an occupant in the midst of

assisting an injured or handicapped other?

(9) Determine whether an occupant co-occupies a spatial subdivision

with one or more other occupants. Determine whether co-occupants of a space will share any information about an effective exit route. Determine whether a "consensus exit or choice" is agreed upon by co-occupants

of a space.

(10-12) Determine probabilistically whether the non-handicapped

individual assists the handicapped other(s).

(13) Assess for the occupant his distance from the threat (if

known to him), and to his current sub-goal (if identified), and determine

whether his most recent move or action improved or worsened his situation. Alternatively, assess the occupant's egress progress relative to

elapsed time spent in the threatening environment.

(14) Determine whether an occupant will discount or eliminate a particular move alternative on the grounds that it is already occupied

by too many other persons (is "crowded").

(15) On the basis of his interruption, intervention, and evaluation status or outcomes, an occupant's next likely move is biased and

probabilistically selected to reflect one of the following: threat evasion; exit or goal seeking; bystander intervention; interruption mode

fulfillment; confusion.

(17-18) Print-out the progress of the simulation run, by timeframe, including: location of each occupant at time t; move probability

values for all move alternatives confronting each occupant, during the

given time-frame; selected move by, and resulting location of each occupant at, time t + 1. Also provide status histories and traces for specified variables, for use in subsequent statistical analyses.

(19) Update those parameters for each occupant which describe his

current status or condition, and which must be recycled as initial values

for the next time -frame.

12
2.2.3 Operating the Simulation

BFIRES is currently operational on a 32-bit minicomputer at the

Center for Building Technology, National Bureau of Standards. Experiments on the program may be conducted at any facility featuring the

FORTRAN-V compiler. Approximately 65,000 bytes of storage are required

to run the simulation. Instructions for the program’s use are provided

in Appendix D.

Although the skeletal configuration is frequently being enhanced

and modified, the current program is capable of a wide range of experimental applications directed toward validating, expanding upon, or modifying the underlying model of human behavior in fires. In this context,

it may be wise to consider a computer, loaded with BFIRES, as a "laboratory" for experimental work. Let us review the features of this laboratory .

Independent Variables

As detailed above, three categories of parameters are adjustable

by the researcher: Occupant ("subject") variables, environmental

("setting") variables, and simulation/system variables. By pre-setting

the values of these, iterations of the stochastic simulation program will

produce distributions of dependent measures. Experimental designs may

be of the "analysis of variance", or factorial format, and substantial

increases in design complexity may be achieved at relatively little

expense.

Dependent Variables: Simulation Outcomes

In its present configuration, BFIRES allows the study of two categories of dependent variables, or simulation outcomes. First, and simplest, is the measure of egress time for occupants. This refers to the

number of time-frames required by any simulated occupant to reach a predetermined exit or other place of refuge. Although the problem of calibrating the program has not been dealt with in any detail, preliminary

simulation experiments do suggest that a "time-frame" could be construed

within the range of 5-10 seconds of real-time. We could run the simulation for any number of time-frames, and record the number of frames actually required by occupants to escape. A straight-forward analysis of

variance, in this case, would permit us to evaluate the effects of independent variables, and of their interaction, within the artificial

environment of the computer simulation .

Second, the program permits us to trace the history of emergency

egress scenarios , as these are generated over time in accordance with the

model. Unlike egress time, which is simply measured, scenario development is complex and multi -variate in structure. For example, at least

four important components have already been identified during the course

of preliminary work with the current program. These include: (1) route

(path shape; patterns of route-switching); (2) use of available time

13
(proportion of time spent, e.g. , remaining in place, helping others,

etc.); (3) manipulation of the physical environment (e.g., opening and/

or closing doors); and (4) patterns of communication among occupants

(e.g., sharing information about the location of exits, etc.). Evaluation of egress scenario development is seen to require multi-variate

analytical techniques.

14
}

15

FIGURE

2.1

Conceptual

Diagram

Illustrating

the

Interaction

of

Behavioral

Subsystems

in

Markovian

Time/Space
SYSTEM STRUCTURE

(WHAT DOES EACH

PART DO")

SYSTEM STRUCTURE

reflected in

SUB-ROUTINES

FIGURE 2.2

PERCEPTION OF ENVIRONMENT DECISION-MAKING ACTION

Each occupant

perceives the current states

of the physical

end social environnent*

'Each occupant

ssess available

action alternatives,

and aahes a decision

about his next action

Social

CffD'JP INTRPT

1 OTHERS EOUALZ

AGREE TBIAS

I BYSTND EB1AS

• JAMMED HI I AS

1 s

Physical

EVAL" CROSS

jXach occupant

(selects an

liction, and ^(•.hereby affects ^.changes

ASSIGN

DOORS

DOORS2

EXECUTIVE

(TIME LINE)

T+l *

Relationship Between Subroutines and the Model's Structure

16
( 1 )

FIGURE

(2)

( 3 )

( 4 )

( 5 )

( 6)

( 7 )

( 8)

( 9)

( 10 )

( 11 )

2.3 Event Control by the Executive Program

17
FIGURE

( 12 )

( 13 )

( 14 )

( 15 )

( 16 )

( 17 )

( 18 )

( 19 )

2.3, Continued

18
OTHERS

GROUPEXECUTIVE

KTOXY

FIGURE 2.4 Subroutine Calling Pattern

19
3. BEHAVIORAL ATTRIBUTES OF THE COMPUTER PROGRAM

3.1 Overview

In the long-range, the criterion against which the simulation will

most frequently be judged will be its ability to accurately and reliably

replicate real-life events. But even this criterion is subject to various interpretations. The architect, for example, may be interested in

fire outcomes , only: For a building with a given configuration, what is

the probability that all occupants will escape injury from a particular

fire event? Graphic designers, and professionals charged with the development of emergency public-address messages, however, may have other

requirements: During the fire, what is the role of information? What

kinds of information are most useful, at which points during the emergency should information be introduced, and how will it influence emergency behavior? Finally, behavioral scientists are likely to approach

the building fire problem with yet another set of questions: How do

humans cope with life-threatening events? How do they make rational

decisions under the stress of a life-threat?

Ideally, any simulation developed as a research and design tool

must be sensitive to the diverse needs of its potential users. Toward

the achievement of this ideal, the present simulation program contains

algorithmic expressions of human processes believed to generate behavioral streams during building fires . The program thereby generates data

which enables the architect to study the end-results (outcomes) of fires,

and the behavioral scientist to study the contributory processes, as each

may require.

However, it would be inappropriate to assume that the results of

the current simulation exercise represent such an ideal. For one thing,

the available literature on human behavior in fires — while replete with

anecdotal accounts and data on various actions people say they take —

gives extremely little insight into the processes by which occupants

gather, utilize, and act upon information during the relatively short

period of the fire event. With so little known about the nature of

these processes, how can they be written into computer algorithms? The

answer to this question lies in the application of available theory and

data, and in a substantial dependence upon this researcher's own intuition and conjecture.

A combination of approaches were applied at different levels of the

problem. First, a general framework for explaining human behavior was adopted from the "information processing" school of cognitive and perceptual psychology. Accordingly, the entire simulation is based on the

notion that the fire environment is the source of information upon which

occupants" actions are completely based. This information enables occupants to contemplate and evaluate alternative courses of action which may

become available as the fire event progresses over time. As occupants

take actions within or upon the fire environment over time, these actions

20
alter the environment, resulting in a continually changing information

field. This psychological framework gave rise to the non-stationary

Markovian structure of the simulation model and program.

At the less general level of decision-making and response to particular information and stimuli, a probabilistic or "best bet" approach

(Brunswik, 1956) was loosely followed. Accordingly, an occupant is

thought to "weight" information about certain alternative courses of

action more than other ones, on the basis of his perception or knowledge

of the overall emergency situation at that point in time. Since, according to Brunswik, neither the available information nor the occupant's

perceptions can ever be "perfect", he assigns probabilities to the action

choices as a function of information currently on-hand. Finally, the

individual samples from among the weighted action alternatives: Given

an imperfect system, his choice may be relatively "good" (adaptive, goaldirected action), or it may be "bad".

In at least one instance, specific behavioral mechanisms were programmed by converting psychological models directly into computer algorithms. The chief application of this technique to the current exercise

concerns the expression of altruistic behavior (Latane and Darley, 1970).

Most frequently, however, algorithmic expressions of specific perceptual or behavioral mechanisms were programmed on the basis of intuition and qualified speculation. In many cases, this procedure was assisted by anecdotal accounts of real fires. More often, however, these

accounts provided a means for checking the "face validity" of a priori

conceptualizations.

In sum, then, the simulation program stands as a collection of

interrelated hypothetical propositions (i.e., it is a theory) about the

manner in which people respond to building fires. Let us now focus on these propositions, as they are "programmed into" the various subroutines

comprising the simulation package.

3.2 Subroutines Which Simulate Perception and Information Gathering

3.2.1 Subroutines GROUP, OTHERS and AGREE

Function . Subroutine GROUP is a collection of programs which

establishes the social environment of occupants as they progress through

the simulated fire event. By calling upon the subroutines OTHERS and

AGREE, the GROUP package informs a given occupant: (1) whether any other

occupants co-occupy the space with him, (2) whether any of the others in

the space possess information currently unknown to the occupant under

consideration, (3) whether any of the others in the space is injured

or otherwise in need of assistance, and (4) whether all the occupants

in the space are able to agree upon an effective exit route.

21
The issue of exit route agreement among co-occupants of a space is

managed through Subroutine AGREE. Three conditions are possible:

First, all occupants of the space may hold the same perception of the

situation, and already agree upon the "best" exit route. Second, none

of the occupants in the space may have any notion of an exit route (perhaps because none were informed about the fire's location, because none

have any familiarity with the location of exits, or because none have

previously come into contact with more knowledgeable individuals).

In this case, no single occupant has any special knowledge to share

with the rest, and they each continue making decisions in the absence

of information about exit routes.

The third condition is less straightforward. In this case, some occupants may have one perception concerning a best exit route, while

others have another perception. Where this condition arises, Subroutine

AGREE attempts to arrive at a "consensus exit cf choice.” This consensus

is reached if and only if 60% (or more) of the occupants share one of

the perceptions. When this rule is satisfied, all the remaining occupants in the space change their perceptions, so that all agree. If

this rule is not satisfied, then consensus is not attained, and all

occupants in the space lapse into a no-perception state: they no longer

know which exit route is best. Moreover, they will continue in this

state until they enter into some other group whose occupants are able

to force a consensus exit of choice upon them.

Background and Underlying Assumptions . Through its functions of

Informing the occupant about the existence of other persons within his

immediate environment, of providing certain information about these others, and of communicating with them, the GROUP package serves as the

occupant's sensory and communicative apparatus. These are highly deterministic routines, in the sense that a given space either is or is not

co-occupied by several persons at once, occupants either do or do not

possess certain kinds of information, they either are or are not injured

or handicapped, etc. The program provides information only in this

binary manner, ignoring the possibility that occupants 9 perceptual mechanisms may occasionally distort the "facts."

Subroutine AGREE, on the other hand, raises issues much more relevant to the interpretation of information from the social environment.

For example, when an occupant confronts several others in a space, and

each agrees upon the best exit route, then each may interpret such agreement as a reinforcement of his own initial perception. Perhaps he feels

"more right" than he did before, since now he finds others who agree

with him. But when there is disagreement among co-occupants, who is

right? The "consensus" notion written into AGREE assumes that people

can be convinced they are wrong, if the pressure to change is sufficiently great. It further assumes that, in those cases where no consensus is attained, a person will develop stress resulting from the inability to replace his eroded perception by a better one. According to

AGREE, this stress is manifest in the form of "goal-seeking in the

absence of any specific goal," or wandering.

22
Unfortunately, the literature on human behavior in fires provides

no empirical evidence relative to inter-occupant communications during

a fire event. Consequently, no means for examining the face validity

of the consensus concept, or of the arbitrarily chosen 60% cut-off point,

are readily available at this time.

3.2.2 Subroutine BYSTND

Function

If an occupant finds that he co-occupies a space with

an injured or handicapped other occupant, then Subroutine BYSTND will be

called. The purpose of BYSTND is to determine probabilistically whether

he ignores, approaches, or remains to assist the handicapped individual.

Subroutine BYSTND is currently under development, and is not included

within the version of the program reported here.

Background and Underlying Assumptions . When written and included

within the simulation package, BYSTND will fulfill the function expressed

by the bystander-intervention model of Latane and Darley (1970). Here,

the likelihood that an occupant will intervene will depend upon such

factors as (1) the number of other "healthy" individuals in the immediate

vicinity, (2) the relationship of the injured person to the bystander,

and (3) the bystander's perception of the "cost" (in terms of, e.g. , time

lost) of the interruption to goal-seeking which may result from helping.

According to Latane and Darley, for example, a bystander may be

quite likely to intervene if he is the only other person available.

If many potential helpers are present, he may well decide that someone else — perhaps more qualified than he — will certainly stop and render

assistance, and that it will not be necessary for him to interrupt his

own routine. If the bystander knows the injured person, however, he may

be compelled to stop (or seek other assistance, etc.), regardless of how

many others are present.

3.2.3 Subroutine JAMMED

Function . As occupants move about during a simulated fire, the population density of the different spatial locations varies. Some mechanism is necessary which enables an occupant to gather information about

the density, or degree of "crowding," of locations he may wish to enter.

Subroutine JAMMED satisfies this function. As an occupant looks ahead

and scans the alternative target locations available to him, he counts

the number of other persons already occupying each. If, for any alternative location, this number is greater than his pre-set crowding tolerance, he rejects that alternative from his array of movement choices.

Background and Underlying Assumptions . The simulation program contains no "queuing" mechanism, as such, for the purpose of regulating the

flow of persons between spatial locations. Queues are used in simulation

whenever the number of elements requiring passage through a channel is

23
greater than the channel's capacity. Typically, waiting elements line

up in the queue, and remain in line until their turn comes to move into

the channel. In the most simple case, once an element has joined a queue, its immediate future is fully determined: It waits its turn, and

then moves through the channel.

It is not clear, however, that persons in stressful situations will

respond to crowding in so mechanistic a manner, oblivious to events

around them. An individual may first seek passage through a particular

doorway, but upon finding the vicinity of the door to be heavily crowded,

may not join any "queue" at all, but rather seek some other route. Moreover, seeking an alternative egress path may not be a straightforward

task, if the occupant is not actually aware of any. If there are no alternatives available, or if the occupant does not perceive any, his

level of stress is expected to increase, as is the likelihood he will

engage in non-adaptive behavior. The current version of the simulation

program manifests such behavior either as goal-less wandering, or remaining "frozen” in place. The latter behavior may continue until the occupant perceives a significant reduction in the crowding of the space leading to his doorway objective.

3.2.4 Subroutine KPOSS

Function . As an occupant moves through a bounded environment, locomotion in certain directions may be possible while in others it may be

constrained. When he arrives at a particular point in space, the individual begins looking ahead and scanning possibilities for his next move decision. He requires a perceptual apparatus which permits him to distinguish open paths from those constrained by walls or other physical barriers. As Subroutine GROUP provides the occupant with means of perceiving

his social environment, Subroutine KPOSS provides "eyes" through which to

discern his immediate physical environment: Namely, as he scans each

potential move alternative, k, he determines which are physically possible to attain, and which are physically constrained. Figure 3.1 illustrates this distinction, as it is defined within the computer-simulated

environment.

In addition, KPOSS responds to inputs provided by Subroutine JAMMED.

A spatial location which is crowded beyond an occupant's level of acceptance will be treated as though it was blocked off by an inanimate physical barrier: the individual will eliminate that alternative, k, from the

array of those available at the current point in time.

Background and Underlying Assumptions . Subroutine KPOSS makes use

of a relatively simple stimulus-response function, in which information

from the environment results in a specific behavioral outcome: Namely,

whenever a move alternative, k, is perceived to be impossible to attain

(i.e., it is overcrowded, or blocked by a physical barrier), it is

removed from the array of alternatives, and is accorded no further con- sideration by the occupant during the current time frame. Whenever an

24
alternative is perceived to be possible to attain, the occupant "stores"

it away for comparison against other alternatives, and includes information about it in his current decision making task. This function is programmed to remain constant throughout the simulation, and not to alter

through interactions with other psychological or environmental processes.

In its present form, moreover, Subroutine KPOSS assumes a clear

visual field. Future versions will require a capacity to accommodate

such intrusions to the visual field as smoke of varying density. The

inclusion of such a vision-reducing medium would result, operationally,

in the introduction of a stochastic component to Subroutine KPOSS: the

probability of a correct perception of the immediate physical environment

would decrease as smoke density increases.

3 . 3 Subroutines Which Simulate Information Processing and Decision

Making

3.3.1 Subroutine INTRPT

Function . INTRPT probabilistically determines whether an occupant's

goal-directed behavior will be interrupted during time-frame t. The current version of the program permits two modes of interruption: remaining

in place, and backtracking.

If the occupant is currently in the backtracking mode (initiated by some previous interruption), then INTRPT is ignored and he is permitted

to return to his origin point uninterrupted (refer to Section 3.3.2:

Subroutine BACKUP). For non-backtracking occupants, an interruption of

either mode may be initiated when a pseudo-random number is compared with

each occupant's interruption probability. Occupants are assigned probabilities of encountering remain-in-place interruptions, backtracking

interruptions, and no interruptions at all. These probability values

may vary across occupants in the simulation, and for each occupant, the

three values must sum to unity. Each occupant is also assigned an interruption limit. If, during the course of the simulated event, an occupant

has experienced a number of remain-in-place (or back-tracking) interruptions equal to his limit, he will not "tolerate” — and hence not experience -- any more; that is, he will ignore future "temptations."

Background and Underlying Assumptions . According to Simon (1967),

interruptions to goal-seeking behavior provide useful means for introducing emotional controls over behavior, particularly in a computer-simulated environment. Interruptions, detours, etc., require the individual

to temporarily suspend his goal-seeking activity. In a stressful

environment, this should lead to increased stress for the occupant,

resulting in an increased likelihood of maladaptive behavior, and a decreased likelihood of ultimate success.

Within the fire environment, the causes and effects of interruption

may be difficult to specify exactly. The empirical literature on behav25
ior in fires is of virtually no assistance on this subject. Consequently, Subroutine INTRPT is extremely simplistic in its handling of

this phenomenon.

Only two forms of interruption are considered. Remaining in place

is meant to simulate any response in which the normal decision-making

processes are bypassed, and through which an occupant rejects all other

move choices available to him. But INTRPT makes no attempt to simulate

specific causes of this form of interruption, which may be cognitive,

physiological, or environmental in origin. Backtracking is meant to

simulate the frequently noticed phenomenon in which an occupant will

return to some earlier location to, for example, retrieve valuables,

close doors, etc.

In sum, the purpose of Subroutine INTRPT is to introduce the fact

of interruption into the simulated environment, so that occupants'

responses to such obstacles to goal-seeking may be observed and compared

against real-world behavior.

3.3.2 Subroutine BACKUP

Function . If a backtracking interruption is evoked by Subroutine

INTRPT, then BACKUP is called. BACKUP processes occupants who have

entered into this mode by retracing their steps back toward their initial

starting location (or toward some other, user specified goal). Once an occupant has returned to this point, he is removed from the backtracking

mode, and he resumes the normal decision-making and goal-seeking processes.

Background and Underlying Assumptions . The main purpose of Subroutine BACKUP is to "waste an occupant's time" by causing him to temporarily suspend his egress behavior and instead move toward some seemingly

irrelevant location on the floor. The current version of the program

affects this activity by retracing each and every step the occupant has

already taken, until the goal of backtracking has been attained. This

is a rather simplistic approach, since once an alternative goal has been

selected, the occupant is more likely to determine an effective route

through the usual decision-making process, than he is to mechanistically

retrace his steps. But even this is an empirical question, for which

little guidance exists in the fire literature.

3.3.3 Subroutines ASSIGN, D00RS1 and D00RS2

Function. The model assumes that an individual's decision-making

behavior will be biased, and that the direction of bias will be determined by his immediate perception of the fire situation. The primary

function of Subroutine ASSIGN is to "recall" for each occupant all those

factors which comprise his current perception (e.g„, current evaluation

of success, knowledge of exit locations, interruption status, etc.), and

26
then to "assign" his decision-making task to an appropriate biasing routine .

For example, if an individual has perfect knowledge of an effective

exit route, and if he perceives no social or physical obstacles in his

immediate environment, then ASSIGN will shift his decision-making task to

a routine which biases him toward seeking his exit goal (see Subroutine

EBIAS, below). Similarly, if an unencumbered occupant has no knowledge

of an exit route, then his decision-making task may be assigned to a routine which biases him toward threat evasion (see Subroutine TEXAS, below).

In its current form, ASSIGN accommodates five biasing objectives:

(1) threat evasion (Subroutine TBIAS);

(2) exit goal seeking (Subroutine EBIAS);

(3) bystander intervention, or "helping" (Subroutine HBXAS);

(4) interruption mode fulfillment (Subroutines INTRPT and/or

BACKUP)

(5) confusion, or no specific bias (Subroutine EQUALZ).

When control of an occupant's decision-making task is assigned to a given biasing routine, there is no certainty that his final move decision

will be in the direction of the biased objective. The term "bias” was specifically selected to underscore the probabilistic notion that, under

a given set of state-defining conditions at time t, certain move deci - sions are more likely than are others.

Two additional functions are controlled by Subroutine ASSIGN. One

concerns the actual move decision. The biasing routine supplies ASSIGN

with a set of probability values. Each value corresponds to the likelihood that a given move alternative will be selected, in accordance with

the particular weighting scheme imposed by the biasing routine. Any

move alternative perceived by the occupant as impossible to attain is

automatically given a zero probability of selection. For each occupant,

the sum of the selection probabilities for all alternatives currently

available must equal 1. Once the selection probabilities have been

established, they are cumulatively compared against a pseudo-random

number. This process results in the selection of a unique move. At the

conclusion of move selection, control is passed to various "bookkeeping"

routines, and a new cycle of perception and decision making begins.

As a part of its move selection function, ASSIGN controls the manipulation of doors by occupants. For example, when an individual encounters a closed door, there is some probability he will open it and some probability he will not. If he chooses not to open the door, the throughdoor alternative is deleted, and the probability values of remaining

alternatives are adjusted so as to maintain a sum of unity. This function

is controlled by Subroutine D00RS1.

Moreover, if the occupant indeed passes through an open door, he may

or may not close it behind him. Subroutine D00RS2 controls this behavior.

27
Background and Underlying Assumption . The biasing aspect of Subroutine ASSIGN is intended to introduce the concept of weighting, based on perceptions of current events, into the simulation program. The ''biased"

probability values assigned to move alternatives provide a means for

achieving Brunswik's "best bet": in a given situation, a person will

probably — but not certainly — respond with a particular action. Similarly, the stochastic aspect of ASSIGN assures that in a certain proportion of cases, an occupant will make a wrong decision — even in the

presence of "good" information.

The inclusion of a door manipulation function is a direct response

to the frequently raised issue of door-closing during fires. In particular, while doors may provide effective barriers to smoke and fire, they

also serve as perceptual barriers which may limit communications and

other forms of information flow. The ultimate question of whether doors

should best remain open or closed is seen here to remain an empirical

one, and it is hoped that its study will be enhanced through the door

manipulation routines included in this simulated program.

3.3.4 Subroutine EQUALZ

Function . Subroutine EQUALZ is one of the biasing routines available to ASSIGN. The function of EQUALZ is to satisfy the condition of no bias , that is, the condition in which the probability values of available move alternatives are equalized.

Background and Underlying Assumptions . EQUALZ is called whenever

the current events perceived by an occupant make it difficult (or impossible) to decide upon a specific objective (e.g. , to evade the threat, to

seek an exit, to assist a handicapped other). The principal assumption

behind EQUALZ is the notion that certain combinations of information will

cause an individual to be (at least momentarily) confused, and that this

confusion will make goal oriented decision making difficult.

The program launches such a state of confusion whenever a mobile,

uninterrupted individual makes a negative evaluation of his current safety

status, and is unable to discern an effective egress route. An occupant

will remain in this state until he:

(1) makes a positive evaluation of his current safety status; or

(2) learns about an effective egress route; or

(3) decides to help a handicapped other; or

(4) decides to enter the backtracking interruption mode.

When a "confused" occupant selects a move from among the equalized

alternatives, this move is not considered to be toward a particular goal.

Rather, it is viewed as "wandering," and this is construed as maladaptive

28
behavior. EQUALZ generates this behavior when a state of insufficient

information is coupled with a negative perception of current safety status. Other causes might include "cognitive overload" (Cohen, 1975;

Saegert, 1976), in which an individual is forced to respond to more

information than he is capable of processing within a given period of

time. This problem may be further complicated if there are conflicts

within the information field. In the future, routines generating maladaptive behavior will have to accommodate these additional complications.

3.3.5 Subroutine TBIAS

Function . Subroutine TBIAS effectuates "threat evasion" movement

behavior. Whenever this biasing routine is assigned, it establishes move selection probability values in such a manner as to "favor" moves which

maximize the occupant's distance from threatening stimuli — fire or smoke

Background and Underlying Assumptions . As an occupant scans the move alternatives, k's, currently available to him, he "measures" the

distance, DIST(k), between each alternative location and the location

of the threat. The selection probability of alternative k increases as DIST(k) increases. That is, the more threat-reducing an alternative is

perceived to be, the more likely is the occupant to select it.

The program assigns an occupant's decision-making task to Subroutine

TBIAS if and only if he:

(1) is mobile and uninterrupted during the current time frame;

(2) is not currently assisting a handicapped other;

(3) is operating under a positive perception of his current safety

status

(4) has no exit route "in mind"; and

(5) knows the location of the life threat.

With neither a specific egress route objective, nor current perceptual encumbrances, the only remaining goal is seen to be that of threat

evasion. TBIAS will bias the individual's movement behavior away from

the threat, even at the expense of moving him farther from an exit.

3.3.6 Subroutine EBIAS

Function . For all move alternatives available to an occupant at a given point in time, Subroutine EBIAS weights move selection probabilities to favor moves which minimize the occupant's distance from an exit

or exit goal point. If any of the available alternatives represents

29
final attainment of an egress goal, then this move is automatically

selected. If more than one alternative satisfy this criterion, then

their probabilities of selection are equalized.

Background and Underlying Assumptions . As an occupant looks ahead

to the move alternatives, k's, currently available to him, he "measures”

the distance, DIST(k), between each alternative and the location of the

next egress objective along his selected route. The selection probability of alternative k increases as DIST(k) decreases. That is, the more goal-directed an alternative is perceived to be, the more likely is the

occupant to select it. Note that egress routes are subject to change

during the course of the fire event, as the occupant receives new information (e.g., he may change his route, midstream, to conform to a new

group consensus). EBIAS establishes move selection probabilities on

the basis of an occupant's current route.

An occupant's decision making task is assigned to Subroutine EBIAS

if and only if he:

(1) is mobile and uninterrupted during the current time frame;

(2) is not currently assisting a handicapped other;

(3) is operating under a positive perception of his current safety

status; and

(4) has a specific egress routine in mind.

3.3.7 Subroutine HBIAS

Function . Subroutine HBIAS has not as yet been developed to the

point of inclusion in the current program version. Its function, however, will be to bias an occupant's move selection toward helping handicapped others. The proposed subroutine will effectuate three categories

of helping behavior:

(1) movement toward an identified, though distantly located,

handicapped (or injured, etc.) other; or

(2) remaining with a handicapped other at a given location; or

(3) assisting a handicapped other to move along an egress path .

HBIAS is assigned if and only if Subroutine BYSTND (the Latane and

Darley bystander intervention routine) orders the occupant to, in fact,

render some sort of assistance. HBIAS determines the mode of assistance

which is likely to be rendered.

Background and Underlying Assumption . The basis for determining a mode of bystander assistance is treated as a function of the relative

30
locations of the helper and the handicapped other, and of the mobility

potential of the handicapped other. Where the two occupants are separated spatially by some distance, the decision to help will require

movement by the bystander toward the individual needing help. However,

where the two persons already occupy the same spatial location, such

movement will be unnecessary.

Just what sort of assistance the bystander renders once he reaches

the handicapped other will depend upon the other's current abilities.

If, for example, he is totally immobile, HBIAS requires the helper to

remain with him throughout the duration of the fire event. The version

of this routine now under development does not allow the helper to go

and seek other assistance, nor does it permit a helper to change the

handicapped other's mobility status. In those instances where the handicapped other is mobile to some extent, then HBIAS moves both the two

persons — at half the normal speed — along a route selected by the

helper (all the earlier rules controlling egress route knowledge apply

here )

Once developed and included within BFIRES, Subroutine HBIAS will

serve general purposes of:

(1) informal rescue of, e.g. , a loved one;

(2) more formal helping procedures established within, e.g., healthcare facilities; and

(3) vicarious bystander intervention.

3.3.8 Subroutine EVAL (EVAL8, EVAL2fl)

Function . In certain cases described above, the direction of move probability biasing (i.e., assignment to a particular biasing subroutine)

depended upon an occupant's current evaluation of his own safety status.

Safety status is here defined as the spatial location of the occupant

with respect to the locations of the threat and an effective exit (when

these are known to him). Evaluations may be positive or negative: A

positive evaluation results whenever an occupant perceives his safety

status to have improved, over a specified period of time.

In its present form, BFIRES provides two alternative status evaluation mechanisms. For any given simulation run or experiment, only one

of these is actually used. The program's user, however, can call either

option. Accordingly, a direct examination of differences between the two

definitions of the evaluation process, are possible.

Subroutine EVAL8 constructs evaluation outcomes purely on the basis

of straight-line distance measurements between an occupant's current

31
location of threats or exits, or both (depending on which of these latter

locations are actually known to him at time t). A positive status evaluation results whenever the occupant's perceived status at time t is

"better" than that at time t-1.

Subroutine EVAL20, on the other hand, evaluates egress progress

relative to the total elapsed time an occupant has spent in the threatening environment. A move in a seemingly threatening direction may not be

perceived as negative, if the occupant thinks — on the whole -- he still

has an ample amount of time left to escape safely.

Background and Underlying Assumptions: EVAL8 . The distance measurement approach is adapted from the simulation by Wolpert and Zillmann

(1969). Their study focused on the "theater fire", or more generally,

fires in large, single spaces. In these settings, occupants, regardless

of their particular locations within the space, are continuously provided

unobstructed visual access. Accordingly, the assumption that occupants

are able to judge relative distances, and hence may make evaluations

based on these judgments, seems a safe one.

Ultimately, however, BFIRES is expected to be useful in a much wider

range of problem contexts, and most f i^es-of -interest subjected for simulation will be in multi-spatial facilities. The question arises, then,

as to the ability of occupants to judge distances when visual access to

key elements (fire and exits) may in fact be blocked? The major assumption of EVAL8, therefore, is that occupants who have up-to-date information about the location of the key elements form "mental maps" of their

environment (see, for example: Moore and Golledge, 1976; Downs and Stea,

1973).

Their judgments of distances derive from these maps, rather than

from direct visual experience. When information is not complete, it is

less likely that EVAL8 will yield a positive evaluation.

Background and Underlying Assumptions : EVAL20 . The approach taken

by EVAL20 avoids any dependency upon distance estimation, and obviates the

need to distinguish between distance judgment through direct visual experience versus by means of a mental map. EVAL20 is based on the notion that

each occupant has his own perception, or "feelings about" the total amount

of time he has to escape the danger zone. This perception may change as

the fire event progresses (he may envision more or less time available).

Regardless of what actions he may take, he will maintain a positive status evaluation as long as he is well within his perceived time limit.

Negative evaluations will result when the occupant approaches his time

limit, and realizes that there may not be enough time to reach an identified exit goal.

In this case, a negative evaluation may be interpreted as the frustration one encounters when he realizes he has reached his time limit,

but has failed to achieve his goal. By returning a negative status

32
evaluation, EVAL20 causes the occupant to respond to this frustration

with non-goal directed movement behavior (wandering).

Unlike EVAL8, which faithfully generates incremental status

ations for each time frame individually, EVAL20 implies that the

starts off with an overview of his situation: "There is only so time available to accomplish the egress task; how will I use it,

much flexibility is there?"

evaluoccupant

much

and how

33
currently occupied location

FIGURE 3.1 Possible vs. Impossible Spatial Movements

person-occupiable location

possible move

impossible move (i.e., blocked)

34
4. NON-BEHAVIORAL SYSTEM OPERATIONS

4.1 Executive Program

The BFIRES Executive fulfills three principal functions:

(1) initialization of simulation parameters; (2) transition of occupants 5 decision processes from state to state (i.e., time-frame to time-frame);

(3) completion of the desired number of replications of a simulated

building fire event.

The initialization of simulation parameters involves reading various

input data into the computer. Three catagories of data must be input,

before any simulation experiment can begin: environmental descriptors;

occupant descriptors; system parameters. Environmental descriptors

include the layout of walls and doors (expressed in terms of x, y coordinates), special characteristics of doors (whether each is initially

opened or closed; whether it is of the manually or automatically closing

type), and the initial location of the fire threat.

Occupant descriptors permit the computer to differentiate between

individual "persons". Occupants may differ from one another on the basis

of their interruption tolerance limits, bystander intervention tolerance

limits, initial mobility status, initial knowledge of an effective egress

route, predispositons toward opening and closing doors, and initial location on the floor.

Finally, system parameters are initialized. These tell the computer

how many replications of a simulated fire are to be executed, the length

of each replication (in time-frames), and the number of occupants actually

in the simulation. The experimenter also selects a random number seed,

and reads this into the machine.

Appendix C provides a guide to organizing input data for FBIRES. A

sample data file is also shown.

4 . 2 Process Updating

As the simulated fire event progresses through a series of timeframes, certain outcomes from the current frame must be used as input

for the next frame. In the present version of BFIRES, this is especially

true about occupants' locations. A time-frame ends when all occupants

have selected new locations, and have moved to them. By changing their

positions in space, they have altered the nature of the environment.

This new information is an important input to the next round of decision

making, during the next time-frame. The conversion of current-frame output into next-frame input is accomplished by Subroutines UPDATE and

NEWXY.

35
A. 3 Grid Transformations

Two systems of spatial notations have been referred to in this

report. The first is the orthogonal x,y coordinate system, through which

the computer keeps track of the relative spatial location of all elements

which have been defined for it (e.g., walls, doors, people). Through

careful calibration, this coordinate system can be appropriately quantified.

In addition, a "k" system has been referred to, particularly with

regard to the direction of alternative moves available to occupants. The

"k" system may be thought of in terms of a compass dial, with eight vectors radiating outward from a central point (note Figure 4.1). This central point denotes the current location of an occupant. Each vector

denotes a possible movement path, in a given direction, toward a new

location. A ninth vector is also possible, when we consider the possibility that the occupant will remain in place. As illustrated in Figure

4.1, each directional vector on the k-grid is designated by a number,

1 through 9. As an individual moves about through space, he "carries"

his k-grid along with him, such that the central point (designated as "K=5") always coincides with his current location.

Subroutine ASSIGN, and the various biasing routines, function in

the "k" system. That is, whenever a move alternative is considered, it

is the k-number (relative to the central point) that is dealt with by the

machine. Once a move is actually selected, however, it becomes necessary

to convert the new location arrived at by movement in the "k" direction

into a point in x,y space. This is accomplished through Subroutine KTOXY

("k-to-x, y" ).

4.4 Reporting Simulation Results

As simulated building occupants progress through the fire event,

various aspects of their experience are monitored and recorded. Three

subroutines report the relevant information to the experimenter. These

are REPORT, TRACE, and TOTALS.

Subroutine REPORT provides a complete occupant summary for each

individual time-frame. For each occupant, this summary includes:

(1) his location at the beginning of the frame,

(2) information about whether he experienced an interruption or bystander intervention during the frame,

(3) what exit goal (if any) he is currently moving toward,

(4) the selection probability values for all move alternatives

currently available to him, and

36
(5) his final location, after a move has been selected.

Figure 4.2 shows a representative sample of output from Subroutine

REPORT.

Subroutine TRACE permits the researcher to re-create the movement

paths of all occupants over any length of simulated time. TRACE output

simply lists, for each occupant, his location in x,y space for each timeframe. With this information, the investigator is readily able to plot

the movement paths. An example of TRACE output is provided in Figure 4.3.

After a simulation run, the researcher might wish to know how many

times a particular event or experience actually occurred. Subroutine

TOTALS keeps track of various events, on an occupant-by-occupant basis.

For example, TOTALS output reports the total number of time-frames each

occupant actually spent in an interruption or backtracking mode. In

addition, it provides the total number of times each occupant passed

through a doorway, during the simulated fire event. While Subroutine

TOTALS reports door-passage data, Subroutine FASSG monitors door-passage

behavior during the simulation. Figure 4.4 shows an example of TOTALS

output.

37
FIGURE 4.1 (a) Relationship Between "X,Y" and "k-Grid" Spatial Coordinate

Systems

38
*» 4->c a 0 •H 10 03 •0 0 CD >,

O'* & > rd

»-i

Q S o CD £ g i—

<v •P in <0 u C H .* o

JX rtf .* rH 03 • o. s 03 Si •H

,, . to o 3 •H m 0 o 0 0 03 rH 5> o PS; *H 03

m

rH •rH o +3

w 0 ii ns •P ns CX xc S ftf «w >1 > c 0 X) 0 u c •P CD w 0 CO T5 -H O

44 4J c o> o ns

a 0 fH 0 •P O' •H rH 0)

•r4 < •H

O

C ts cx > W -p c •H c i o

Sh 0) <8 0 V •H c g

ns > 0 TS c •H a 0 0 •H 03 1 03 £ i—

CD 0 £ O' rH rH P 2, 0 G ,Q

rtf c 4J fO •H •H •H U •rH c 03 4-3 c 03 ->H CD CO 5 o •H 03

03 -P Sh 0 <D fd 0

>i

UM 'Ou 44 e cx

-C 0 3 c u •H 0) (X u U ns < XS CX ns o K 3 1 c * O G 0 g u 0)

0) 0 > 0) -p CD •H

03 03 rH

rH c Q

C/3 0 S .—

*r4 4-3 03 • 03 4-> rH 03

4-> nJ Oh rH 0

ns o O nsC

C 0 • 3 •H -H rH C 03 v— t-H rd 0 03

44 03 •H

G 03

Q

rH 4-3 •rH JH •H 0 Si O rH 03 44 u CO 0) •H O' -H 03 T3 44 ; £h Sh •rH 44 TSl >i 3 0) P ns 0 U 4-3 01 CD PI * O c 0 - 0 •rH 'O

0)

r~l ns 0) G 03 Sh U Ii3; 5 0

03 5

03 •H

03

G1 P ", j >i TS c 0) 4-3 3 JG •o H (X ns c C4 ol

.G«w

rHg3H

IX

39
Y

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

FIGURE 4.1 (d) Spatial Sensitivity May be

Improved by Increasing the

Number of Person-Occupiable

Locations in a Given Space.

This Requires the use of an x,y coordinate grid, which is

smaller with respect to room

size.

- 40
>c^ooci.o^:

Tire - 5

PRIOR EXIT

OCC LOCAT AGREED LOCfiT HUM WD VO 1NT IBYSTD UPON P7DIST TDIST PEDIST EDIST PCI) PC2) PCS) PC4) PCS) PC6) PC?) PCS) PC9) XD YD

0.090 2.626

0.000 0.000

0.000

2.820 2.000

10. 198 2.000

2.000 0.000 2.000

2.880

2.000

0.246 2.000

8.246

0.000

6 . 325l

8.008

0.000 0.000

0.000 0.000

0.000

0.000

0.000

0.000

0.000

0.000 0.000

0.000 8.000

8.000

0.000 0.000

0.000

0.080

0.086 0.124

0.000

0.003

0.000

0 . 2?e 8.000

0.380

0.088

0.000

0.000 8.000

8.000

0.000 0.25?

0.000

0.257 0.000

0.27?

0.000 6.008

0.ae0 0.000

0.660

0.800

0.000 0.000

0.000

6.600

0.608 0.000

occupant- to- exit

occupant- to- threat

Exit of eheice

In helping mode?

In an interruption mode?

Location at time t

Occupant number

Move alternative selection ——

probabilities

Location at time t+1

FIGURE 4.2 Output from Subroutine REPORT

41
OCCUPANT NUMBER: 7 TIME FRAME X Y

1 14 2 2 14 4

3 14 2 4 14 2

5 14 4

6 14 2

7 14 4

e 12 4

9 12 4

10 10 4

110 6

12 10 S

13 10 4

;4 10 2

15 10 2

16 10 2

17 10 2

18 10 2

19 10 4

20 10 6

21 10 6 22 10 4 23 12 4 24 12 4 25 14 4

26 14 2

27 14 4 28 14 4

29 14 4

30 12 4

The figure indicates x,y coordinates

locating an exemplary occupant in

space, as this location changes from

time-frame to time-frame . This

sample simulation was run for a period

of 30 time-frames (150-300 seconds of

real time)

*o*o»or>*c*T»or*e* *

FIGURE 4.3 Output from Subroutine TRACE

OCCUPANTS INTERRUPTION TOTALS:

REPORTS TOTAL NUMBER OF FRAMES SPENT IN MODES

INTRPT OCCUPANT

TYPE 12 3 4 5 6 7 8 04245552

12 0 8 0 5 2 11 14 Int. Mode #1: remain in place

Int. Mode #2: backtracking

DOOR PASSAGE TOTALS

OCCUPANT

1 2 3 4 5 6 7 8 2272 12 594 Actual number of passages/occupant

END OF TOTALS

END OF RUN

FIGURE 4.4 Output from Subroutine TOTALS

42
5. DIRECTIONS FOR FURTHER STUDY

Both short and long range objectives for research, development, and

implementation derive from the simulation effort accomplished thus far.

In the short range these primarily relate to the heuristic values the

simulation development exercise, namely the use of such an exercise to

identify literatures and data bases, and to generate researchabie questions and issues. Longer range problems (outside the scope of this

report), concern the matters of calibrating and validating the 3FIRES

program, and of implementing it as a building design and regulatory tool.

5.1 The Simulation Exercise as an Identifier of Literatures and Data

Bases

In Section 2.2 of this report, the BFIRES program was described as

a "skeletal" simulation device. This term was used to suggest that the

program expressed a certain core process , even though very little data

may actually be available to elaborate that process. Accordingly, the

function of the skeleton is to provide a framework for: (1) conceptualizing the problem, (2) hypothesizing relationships among important

variables, (3) exposing critical questions which must be addressed in

the field, and (4) identifying literatures and data bases which could

eventually be tapped.

Focusing on this last point, there are two important objectives of

literature and data base identification. First, literatures as yet

untapped may be useful in "filling out" (and/or modifying) the initial

skeletal program. In terms of program development and expansion, in

fact, the application of existing literatures and data bases is an essential first step, preceding the design and conduct of new empirical

research.

The second objective of literature and data base identification

recognizes the current low level of knowledge in the fire field concerning issues of human response. Namely, even in the absence of a well

calibrated or validated simulation program, the exercise of developing

such a program exposes literatures expected to be of utility throughout

the problem area . Accordingly, the exercise yields an "agenda" of literature review and data base evaluation studies which should be undertaken

as precursors to a wide variety of empirical investigations on human

behavior in fires.

This report has emphasized the need to consider building fires as complex events which involve interactions between pyrological phenomena,

building design and layout, and human response. In this section, literatures concerning the latter two areas shall be treated. This presentation is not intended as a literature survey, review, or critique. Rather,

it is offered as a "shopping list" for eventual use in enriching (1)

the simulation program, and (2) the current state of knowledge about

human behavior in fires. For the most part, the literatures were identi43
fied during specific conceptual and program development tasks. Consequently, the direction of literature search may appear somewhat biased

toward the general modeling route actually followed.

As a result of the investigation thus far, five superordinate and

interrelated categories of literature seem relevant to, and necessary for

the expansion of knowledge relative to human behavior in fires. These

are the literatures of (1) environmental psychology, (2) cognitive psychology, (3) stress, decision, and judgment under uncertainty, (4) experimental social psychology, and (5) personality psychology. Let us consider the salient aspects of these.

5.1.1 Environmental Psychology

Environmental psychology is a relatively young branch of behavioral

science, and it may be considered to combine important aspects of many

other branches. It is chiefly concerned with interrelationships between

people and the "designed” environment. During its brief history, several

schools of thought have developed, leading to a variety of theoretical

approaches to the study of person-environment relationships. In the most

general terms, person-environment models will be found to stem from

psychoanalytic, behavioristic, cognitive-developmental, and transactional

foundations

The BFIRES program embodies various assumptions about the ways

building occupants gather information from their physical environment,

how they utilize this information, and how they gather additional information through social encounters. A useful point of departure for

explicating these assumptions will involve a detailed review of the

cognitive and transactional orientations in environmental psychology

(Proshansky, et al. , 1970, 1976; Ittelson, 1973; Ittleson, et al. , 1974).

A positive aspect of research in cognitive and transactional environmental psychology is its utilization of field-based methods, in which

the observation of "real world" behaviors in their "real world" settings

is heavily stressed. This has resulted in research efforts which have

been largely situation-specific, and frequently with a view toward situational - rather than general - theory development. Unfortunately, the

array of situations and settings of interest to workers in this area has

excluded the special problems of building-scale emergencies.

However, useful perspectives on the subjects of environmental

stressors, environmental "pathology", and environment-information overload has begun to emerge from intensive investigation of non-emergency

settings. In particular, one should note the work of Saegert (1975) on

the effects of crowding and information overload on behavior in and

responses to environments; Saegert (1976) on the stress inducing and

reducing qualities of environments; and Cohen (1975) on the effects of

environment-information overload upon information gathering and

processing.

44
Unfortunately, the remaining literatures do not have as a central

theme the "designed" environment. Nevertheless, they may provide enormously rich data bases on psychological processes which operate during

building fires.

5.1.2 Cognitive Psychology

The branch of cognitive psychology generally deals with the study

of central processes, including thinking, decision-making, and problem

solving. Although very recent work in environmental cognition will be

found relevant and useful to the special problems of emergency environments, the environmental researchers have not as yet produced a data

base sufficient for immediate application. A thorough review of several

"traditional" concerns of cognitive psychology is therefore recommended.

Although this field is quite diverse, the simulation effort has

suggested a relatively narrow range for focused attention. Reviews

of the following specific literatures should prove useful in developing

research which explicates the problems of human behavior in building

fires, and of simulating such behavior.

Computing machine — brain function analogy . Perhaps the line

between computers as replicators of overt human behavior and computers

as models of human brain function is a very fine one. However, the differences have been the source of considerable argument within cognitive

psychology, and are relevant to our immediate objectives. Although many

details of this dichotomy need not concern us here, it is important

to distinguish between the computer simulation of behavior , and arti - ficial intelligence.

Strictly speaking, the computer simulation of behavior requires that

complex theories of behavior be faithfully translated, at every level of

detail, into computer programs. Equipped with such programs, then, a computer may be thought of as "behaving" as a person would (assuming

the validity of the theories written into the programs). The BFIRES program is an example in this direction. As an alternative to building

theoretical propositions into computer algorithms, firsthand data about

the behavior of actual people is often available and may be utilized.

The work of Simon, (1969), Newell, Simon and Shaw, (1965), and Loftus

and Loftus (1976) exemplify this general approach, and would form an appropriate basis for literature review.

In contrast, the term artificial intelligence implies that machines

can be programmed or equipped to imitate the overt behavior of persons,

although no attempts may have been made to simulate those inner processes

which result in the overt behavior. This approach has been commonly

employed in the automation of industrial processes, where it is necessary

to replicate the mechanical task performance of human workers, and where

there is little or no need to simulate all the mental processes involved.

More recently, unmanned space travel has provided other important

45
examples. In these cases, computing machines are frequently called upon

to process information, make judgments or decisions, and to perform various tasks in ways which are most reliable and efficient — and not

necessarily similar to the ways these tasks are performed by humans.

The area of artificial intelligence has been explored in great detail

by Boden (1977), who has provided an exhaustive review of the literature.

Additional perspectives have been offered by Raser (1969) and Dutton

and Starbuck (1971).

To the extent that computer methods will be applied to the study of

human behavior in fires, it will be important to deal with the dichotomy

described above. If our only concern, for example, is to produce machinegenerated fire outcomes which imitate those of actual fires, then there

need be no search for knowledge on psychological, social, and environmental processes believed to produce or influence those outcomes. However,

if we require the facility to examine the ways people internally deal

with the fire event as it unfolds, or to test the effects of variations

in person-based factors, then it will be necessary to simulate those

human psychological processes believed to operate during fires.

The application of artificial intelligence to fire research, therefore, is likely to be limited to those problems in which we want to

evaluate physical design influences only. On the other hand, since

behavior-simulation focuses directly upon cognitive processes, use of

this form of computer application should permit the evaluation of such

human-centered influences as level of training, familiarity with the

environment, organizational responsibility, etc.

Information processing . The problem of computer-brain analogy, and

the use of computer programs to model human cognitive functioning center

primarily about information processing theories of cognition. Primary

issues in information processing concern (a) perception and information

gathering, (b) the mediation or "filtering" of selected stimuli in the

environment, (c) the retention of information in short and long term

memory, (d) the formulation and use of action strategies, and (e) the

evaluation of goal-directed actions. Chapters 2 and 3 of this report

have emphasized the importance of such issues in the development of

BFIRES. In addition to literature cited earlier in the report, future

reviews of this area should emphasize investigations of information

retention, recall and application (e.g., Loftus and Loftus, 1976;

Atkinson and Shiffrin, 1968, 1971), sensory-cognitive interaction

(Palmer, 1975), and information integration (Garner, 1974).

Problem solving . Various aspects of human problem solving are relevant to the problems of occupants’ emergency behavior. The BFIRES program itself embodies certain assumptions about the ways people respond

to problems they confront in the emergency environment.

i Eisenstadt and Kareev (1975) have identified several key components

of human problem solving activity, including: (a) scanning behavior

(exploratory searching, hypothesis testing, information-checking);

46
(b) searching through the problem space (state assessment, assessments

relative to past and future states, pattern extraction from repetitive

events); (c) previewing or planning ahead; (d) action selection (selections which involve extensive previewing, versus "blitz planning" in

which little or no previewing is involved); (e) backing up (consideration of new alternatives after a particular strategy has been embarked

upon)

5.1.3 Stress, Decision, and Judgment under Uncertainty

Another important product of the simulation exercise has been its

emphasis on the need to integrate the influence of stress with decisionmaking, when studying human behavior in fires. Several references to

environmental stress research were cited in Section 5.1.1. In addition,

the comprehensive experimental review by Broadbent (1971) provides useful insight into the problems of stimulus detection, arousal, responsiveness, vigilance, selective perception, decision speed, and interaction

effects among diverse stressors.

The literature on judgment under uncertainty should also prove useful. Tversky and Kahneman (1974), for example, studied decision-making

strategies and dependence upon biases and heuristic devices under conditions of uncertainty.

5.1.4 Experimental Social Psychology

Social processes are believed to be highly relevant to the problem

of emergency behavior. Various fire investigations have emphasized this

point by reference to actual data (e.g., Wood, 1972, and Bryan, 1977),

and certain assumptions about interpersonal behavior were written into

BFIRES. The literature and data base in experimental social psychology

are quite extensive, and only small segments of these may be referenced

here. Future investigators may find the data from the following areas

useful in research on human emergency behavior.

In the vast majority of cases, fire will be a uniquely novel experience — and will provide a uniquely novel environment — for building

occupants. Consequently, it should not be assumed that individuals will

have specific adaptive response protocols in mind for immediate application. It is much more likely that each individual will pick up certain

cues provided by other persons in the immediate vicinity, and that some form of social learning or vicarious imitation will occur (Miller and

Dollard, 1941; Bandura, 1962; Bandura and Walters, 1963).

During a fire emergency, social exchanges may play a key role in the

development of effective behavioral response patterns. These exchanges

extend far beyond processes of information transmission, to include social

reinforcement of one's actions. Homans (1961) and Thi'baut and Kelley

(1959), for example, postulated that social relationships will continue

only as long as the parties involved receive certain benefits from them.

Adams and Romney (1959), (1959), on the other hand, found that relation47
ships depend upon the reciprocal reinforcement between people in control

and those being controlled. The literature on social-reinforcement

exchange would seem highly salient to problems of drills and training

plans, evacuation leadership, vocal evacuation systems, and emergency

social organization (Dynes and Quarantelli, 1968).

Of further relevance to these problems is the literature on social

power. French (1956) and French and Raven (1959), for example, were concerned with those small-group processes which lead to opinion change,

and through which certain individuals are "influenced" by the actions of

others. These investigators suggested a three-dimensional model of

social influence, which involved power patterns, communications patterns,

and opinion patterns in the group.

Other salient areas within experimental social psychology which

would be worth tapping include: (a) investigations of cooperation and

competition (e.g., Deutch, 1949; Grossack, 1954; Gottheil, 1955; Shaw,

1958), (b) the literature on cognitive explanations of social processes

(Bruner, 1957; Newell, Simon and Shaw, 1958), and (c) motivational

aspects of social behavior (Krech and Crutchfield, 1948).

5.1.5 Personality Psychology .

It would be difficult to develop a design or regulatory tool of

general utility if it were based to a significant degree upon individual

differences among building occupants. However, where such differences,

traits, or characteristics are found to be important, it will be necessary to construct broad-based tools on the basis of well defined

distributions. Consequently, a review of individual, or personality

psychology is recommended, through which those traits expected to correlate highly with social behavior, environmental response, and decision

making under stress can be identified and investigated.

5.2 The Simulation Exercise as a Generator of Researchable Questions

An important value of the simulation development process is its

function as an illuminator not only of complex phenomena, but of means

for studying them as well. The development of the BFIRES program has

already begun to spawn a variety of questions concerning both the

explanation and study of human movement behavior during building fires.

Certainly, many of the questions introduced below may be asked with

specific reference to BFIRES. Indeed, future tasks involving the

calibration and validation of this program shall endeavor to address

these. The ultimate objective here, however, is to identify questions

applicable across a much wider spectrum of research on human behavior

in fires.

48
5.2.1 The Explanation of Occupant Behavior in Fires

Several fundamental questions concerning the need to explain human

behavior in fires, and the use of certain models for doing so, will

need to be addressed relatively early. The first question is, logically,

why develop explanatory models at all? A model-based system for building

design and regulation might appear to some individuals as a "given."

However, the actual traditions in the fire field have been somewhat

different. Namely, new concepts in life safety design have tended to

stem from the piecemeal analysis of individual disasters. Although the

actual success of the traditional approach may be indeterminate (after

all, there is no basis for comparison), there are currently no data

available to suggest that a model-based orientation would significantly improve life safety statistics.

To a very real extent, the current investigation represents an attempt to establish the credibility of the model-based approach, and

to identify potential values added by it to life safety design and

regulation. Additional questions, then, concern the appropriateness of

specific models or theoretical orientations. If we wish, for example,

to model ongoing human behavioral processes, is the Markov model the

most appropriate basis? Moreover, are algorithmic models appropriate

for simulating human cognitive functions, or should other, more heuristic

alternatives be employed? These questions will require intensive, short

range consideration.

Other issues concern the functional and behavioral bases for writing

computer simulation programs. In BFIRES , for example, the behavior of

the computer is guided largely by loosely constructed theoretical statements and hypotheses about the ways real people would behave under certain circumstances. A very different approach, however, would be to

collect massive amounts of empirical data, and then to translate these

into simulation scenarios and computer programs. Each approach carries

its own implications for research and application: Theory- or modelbased simulations may tend to overemphasize occupants' internal cognitive and behavioral processes (rendering the achievement of design and

regulatory goals less direct); the data-based simulations may be limited

to replicating only gross outcomes of historical fires (making it difficult to assess psychologically-based solutions to life safety problems).

Where the modeling of human behavior in fires is thought desirable,

and where computer simulation seems an appropriate technique, questions

of scale and system boundaries become salient. It will be necessary

to determine the comparative value of general models of human behavior

in fires (of which BFIRES is an example) with respect to those which

are more situation-specific (focusing on the specialized problems of,

say, a single building type). For which classes of problems are each

of these directions most appropriate? What forms of modeling and simulation strategy are most effective for each case? What are the relative

costs?

49
Finally, with respect to BFIRES itself, important research problems

emerge concerning the appropriateness of assumptions and literatures

built into the program, and of variables actually selected for study.

These were described in considerable detail in Chapters 2 and 3 of

this report, and questions relating to their appropriateness, correctness, etc., will only be answered through the conduct of external validation studies which test the ability of BFIRES to simulate real-world

events and behaviors. The entire area of external validation lies outside the current scope. However, if we consider BFIRES to be a composite

of interconnected hypotheses, then both the overall model - and the

individual hypotheses - are available for immediate empirical analysis

in the field. Future developments in the computer simulation of human

behavior in fires will depend upon both empirical analysis of the

antecedent hypotheses, and on concurrent efforts to externally validate

the simulations.

5.2.2 Methods for Investigating Human Behavior in Fires

Although computer simulation is itself one approach to investigating behavior in fires, the simulation exercise underscores certain key

problems associated with other (critically interrelated) forms of

research in this area. In particular, if future simulation research

and applications will require empirically derived inputs, then considerable attention will be required to assure the validity and utility of any

data actually obtained. Problems peculiar to the empirical investigation

of human behavior in fires have already been enumerated and discussed in

great detail by Stahl and Archea (1977), who also identified potentially

useful investigatory strategies.

50
6. SUMMARY

The conceptual development, structure, and function of BFIRES, a computer program designed to simulate human movement behavior during

building fires, have been presented. The technical approach leading to

the development of BFIRES has involved both theoretical model-building

and computer simulation programming.

The basic model underlying BFIRES Is derived from a non-stationary

discrete time Markov process. Essentially, the model postulates that

human occupants construct their emergency responses and behavioral decisions on the basis of socially- and environmentally-based Information,

both of which continually change over time. As the information field

changes, so does the basis for individual emergency decision-making.

The simulation of this process has been effectuated through BFIRES, a computer program written in FORTRAN-V and currently operational at the

Center for Building Technology, National Bureau of Standards.

The specific functions, structures, and underlying assumptions of

BFIRES were thoroughly treated in this report. The basic components

of the program include mechanisms for initializing simulation experiments, algorithms for simulating human perceptual and cognitive processes, and routines for overall system operations and data accounting.

Finally, directions for further study were discussed. Recommendations for future research chiefly involved reviews of literatures and

data bases identified during the simulation development exercise, and

empirical research efforts designed to address certain simulation-derived

questions about human behavior in fires.

51
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56
APPENDIX A: A MARKOVIAN ANALYSIS OF HUMAN BEHAVIOR IN FIRES

A .1 A Markovian Analysis of the Problem

The model suggested here is offered as a hypothetical description

of human movement behavior in response to life-threatening stimuli within

the boundaries of a single building floor. In simplest terms, the model

permits the description of individual and group deci si on -making and movement behavior in a spatial field defined in terms of various kinds of

information. For all practical purposes, we shall take the final "goal"

of egress movement behavior as the point of entry into a stair or other

perdefined refuge zone.

From here on, we are concerned with the argument that movement

behavior of the kind described above is approximated by a form of Markov

process . The Markov process (or Markov "chain", or "model") is a probabilistic model, said to be useful in analyzing complex systems (Howard,

1971; Kemeny and Snell, 1972). The principal compenents of Markov models

are states (specifications of parameters which describe a system at any

point in time and space), and state transitions (incremental movements

of the system from its present state to the next future state). The

fundamental Markov assumption specifies that only the present state of

the process is releveant in determining its future behavior. Accordingly, the probability of making a particular state transition depends

only on the specification of parameters defining the most recent position.

As a result, Markov processes have been referred to as "memoryless". However, it can be said that the present state of a system is

the cumulative result of the system’s "history ", and that this state

can be thought of as containing this history (e.g., the case of transmitting genetic information between generations).

Between any two contiguous point in time, there may be any number

of alternative states to which a system could move. Each of the alternatives possesses a certain probability of selection, and at any point

in time, the probabilities of each of the alternative state transitions

sum to unity. Accordingly, Markov processes are probabilistic, or stochastic models, and state-transition is a stochastic variable.

Our concern is with the conceptualization of building fire systems

as Markov processes. If we consider the fire system to consist of fire,

building, and human components, then we can surely think of these components as undergoing various forms of change over time: The fire itself

goes through various pyrological stages, and it may expand or contract;

portions of the building may undergo structural changes, and various

spaces may cease to permit occupancy; people may move about through the

spaces of the structure, and their movement patterns may show distinct

patterns over time. When we think of the entire fire system as moving

from state to state over time, we are actually dealing with the complex

A-l
interactions between system components, and the complex combinations of

alternative states which could conceivably be selected at any given

instant

We shall focus, at present, on the human component of the building

fire system - the occupants of a building under fire conditions. Even

more specifically, we shall focus upon occupants' egress movement in

space, in response to social and environmental stimuli which continu- ously change over time .

In Markovian terms, we shall consider an occupant's displacement

from one point in space to another as part of a state transition. The

collection of all occupants' spatial displacements during a given increment of time shall be taken as a complete state transition.

Accordingly, if a person occupies location i at- time t, then at

time t+1 he shall have moved to location j (unless, of course, he had

decided to remain in place). Moreover, he will have had to select his

next location from among some range of choices . For each occupant in

the fire, spatial displacement ("move") alternatives may be assigned

probability values, and these may be unique to, and determined by, the

conditions which define the system at time t . That is to say, under

certain social and/or environmental conditions, a particular move alternative may appear quite attractive, and hence be highly weighted. Under

other conditions, the same move might be perceived as highly threatening,

and hence be assigned a very low probability of selection.

At this point, it is necessary to introduce another Markov property

into the discussion: stationarity . We distinguish between "stationary"

and "non-stationary" Markov processes such that:

For the case of stationary processes, state-transition probabilities are not dependent upon time. Therefore, a single matrix of transition probabilities may be specified a priori , and this matrix entirely

defines the Markov process over all time-frames (refer to Figure A.I).

In non-stationary models, transition probabilities are timedependent, implying the existence of a different transition matrix for

each time-frame, and further implying that the process may not necessarily be a priori definable beyond the first time frame (i.e., the out- comes from the first time-frame, t, determine outcome probabilities for

frame t+1, etc.). Refer to Figure A. 2.

The distinction between stationary and nonstationary processes is

extremely relevant to the application of a Markov modeling strategy to

the fire problem. Let us contrast the work of Breaux (1977), who offers

a stationary explanation, with that of Stahl (1975a; 1976) and Nelson

(1976), whose preliminary conceptualizations suggested the need for a time-dependent (non-stationary) approach:
Breaux conducted intensive interviews after a hotel fire in

England. Using an open-ended interview format, he elicited information

from victims concerning what they did during the fire. In reviewing his

data, Breaux found that many actions were reported by more than one victim, and that in many instances, occupants reported having chosen partic - ular actions from among certain alternatives . He referred to these

choices as "degrees of freedom", and noticed that in certain cases occupants may have perceived alternatives which were physically unavailable — and vice versa. He also hypothesized a linkage between egress success, and the availability of action alternatives (degrees of freedom)

in the environment: If a person perceives a reduction In degrees of

freedom as the severity of the fire increases, then he is less likely

to escape than a person for whom at least a minimum number of alternatives had been available.

According to Breaux's analysis, a person's total fire experience

may be defined in terms of particular actions taken along a discrete

time scale. Accordingly, each action point is analogous to a Markovian

'state', and transitions between such states advance the individual’s

expeience through time. Where action choices exist, a probability of

selection exists for each.

It should be possible then, to define the human behavorial aspects

of a fire in a given building type (e.g., Breaux's "hotel") as a stationary Markov process, as follows: Assume that victims from a sufficiently

large number of hotel fires were interviewed in detail about their

actions, and about their perceptions of choices. Assume further that,

except for minor sampling variations, actions and patterns of choice

availability were consistent across the population of hotel fires. Then,

based on the relative frequency of action choices actually made, the

probability that a given choice will be made again under similar circumstances during a future hotel fine could be assigned. By assigning such

values to all action alternatives along the time-line, the complete human

behavioral system relative to hotel fires would be entirely defined by

the single matrix of action-selection probabilities. The Markov process

described by such a matrix is considered stationary, since all selection

probabilities are predefined , irrespective of the actual time demands of

a particular fire. Refer to Figure A. 3.

The practical appeal of Breaux's approach lies in its single-statement, a priori description of a behavioral system: the action-selection

probability matrix. Given such a matrix for, say, hotel fires, we could

predict probabilistically the next action an occupant will take, on the

basis of our knowledge of his immediately preceding action. Moreover,

we could make such a prediction irrespective of the point in time at

which the new action is being selected. Simply put, we need only know

a person's action at time t, in order to predict his action at time t+1
A. 2 Factors Which Complicate the Analysis

As mentioned above, Breaux's application of the Markov methodology

to the prediction of human behavior in building fires assumes that: (a)

a large number of fires in a specific building type were intensively

investigated, and (b) the behavioral outcomes and selection patterns were found to be relatively invariate across all the fires studied. The

latter assumption must be true if the deduced selection-probability matrix

is to be representative of the building type , and therefore potentially

useful in the prediction of future fire outcomes relative to that type.

If this assumption is false, then discrepancies between individual fire cases would yield probability matrices useful mainly as post-hoc descriptions of individual historical events.

However, available evidence on human behavior in fires makes the

question of whether such behavior is describable by a stationary process,

extremely difficult to answer. For example, when we consider actual case studies for which post incident data bases exist, the data may shed very

little light on decision-making over time. While Wood, Bryan and others

reconstructed action sequences from such data, these sequences illustrated

only what people say they did ; no attempts were made to elicit their perceptions of action alternatives (Breaux's "degrees of freedom") during

various stages of the fire experience.

Moreover, various anecdotal accounts of building fires raise the

question of whether consistency of outcomes across a large population of

fires in a given building type should even be expected, at all. Such

accounts often indicated that highly predictable and consistent patterns

of occupant behavior may have been the exception, rather than the rule.

This may be especially true in those occupancies where training is likely

to be minimal, and where occupant vigilance is likely to be at rather low

levels

Finally, even a casual or intuitive consideration of fire development in a building causes us to question models which ignore the ways in

which time-based changes in environmental information influence occupants' actions, and processes of action selection . We may well ask how

occupants' perceptions of the threat-laden environment influence the

likelihood that various actions will be selected, and how this influence

changes as the environment is perceived to change over time?

Nelson (1976) attempted to deal with these questions, by means of a simple conceptual statement: both the behavior of the fire and that of

building occupants should be thought of as advancing along two parallel,

"communicating", time-lines. A "view" of the fire system at any discrete

time frame (t), then, would include descriptions of both the human and

fire components, as well as interactions between the two. A state

transition of the entire system, from frame t to t+1 , would be taken to

encompass the transitions made by each component.

A-4
State transitions of the "fire" component, according to Nelson,

could be readily described, a priori , e.g., ignition (at time t), "flashover" (at time t+i), spread to first adjacent room (at time t+j), etc.

However, the time spent by the fire in each state may be subject to wide

variation.

Nelson further posited specific links between the two parallel

time-lines; that is, the current state of the fire influences occupants*

responses, and these actions in turn affect the physical state of the

fire and architectural environment. Nelson's proposition is outlined in

Figure A. 4. Although fire states are seen as definable, a priori , Nelson's view allows the notion that upon perceiveing the fire in its

current state, a person's actions derive from some mediated selection

process, rather than from a purely deterministic relationship between

the fire environment and occupant behavior. His view further permits

the notion that occupants' action sequences, and their probabilities of

occurrence, cannot be specified a priori ; that sequences of actions

built-up as a result of probabilistic events may render both actions

and sequential patterns unique for particular fire situations. Unfortunately, Nelson never actually specified the nature of the linkage between

occupant response and the fire environment.

A. 3 An Expanded Markov Conceptualization

In an independent effort, I drew conclusions similar to those of

Nelson on the basic interactive structure of building fire systems

(Stahl, 1975a; 1975b; 1975c; 1976). That effort, however, was much more specifically directed toward the explication of human decision-making

processes in life threatening environments. My review of the relevant

literature at that time yielded the notion that a person's egress behavior during a fire emergency depends upon: (a) his perceptions of the

location and severity of the threat; (b) his perceptions of available

action alternatives; (c) his immediate experience with decision-making

in the given situation (i.e. , immediate success/failure history); (d)

interruptions to goal-directed action he may have encountered (whether

of cognitive, social, physiological, or environment origin); and (e) his

level of long-term knowledge and experience (e.g., his familiarity with

the building's egress routes, his previous participation in drill or training programs, etc.). Each of these factors were seen to undergo

change over time, as changes in the environment implied changes in its

perception, and as altered perceptions resulted in variations in the

probability that an action choice might be selected at a given point

in time.

An important outcome of those studies, then, was the idea that the

probability values associated with the selection of action alternatives

are not invariate (and should not, therefore, be predefined a priori ).

Rather, such probability values reflect current realities of the unique

event under study, and are determined "on the spot" by the interactive
network of system components which contribute changing streams of infor

raation about their states. This notion was seen as directly analogous

to an information-processing approach to cognition in emergencies, in

which persons consciously alter the weights they assign to action alter

natives, as information regarding the utility of each changes.
STATES

STATES

1 2 3 4

j n

| 1 p

i?

P

13

P

1 4

P

ln f 2 i

—

CN cu P

22

P

23

P

24

P 02n

3 p ^

4 P 4d

?

4n \

*

n Pnn 1

where P . . is the

±3

probability that a system in state i

will move to state

j, irrespective of

time

FIGURE A.l A Single Probability Matrix Describing a Stationary

Markov Process

where P . . is

ij

determined by

parameters describing

the state of the

system at time t only

STATES

FIGURE A. 2 A Set of Probability Matrices Describing a Non- St ation ary

Markov Process

A-7
STATES

STATES

1 2 3

' 4 3 6 7 e 9 10 " 12 13 14

! 13 16

1. awaken 1.0

2. HEAH SHOUTING 0.7 0.3

3. MISINTERPRET NOISE 0.3 0.3

41. IGNORE NOISE 0.3 0.3

3. CORRECTLY INTERPRET NOISE 0.8 0.2

6. ORESS TO LEAVE 0LOG. 1.0 H

7. LEAVE BLDG. VIA EMERG. EXIT 0.3 0.5

8. ASSIST IN RESCUE EFFORT 1.0 .

9. DIAL 911 1.0

10. SCAN ROOM 0.8 0.2

11. ORESS TO INVESTIGATE 1.0

12. OPEN DOOR 1.0

13. CLOSE DOOR 0.3 0.3

14. GO TO WINDOW 1.0

13. OPEN WINDOW 1.0

16. WAIT TO 0€ RESCUED __L_

The matrix Is reed horizontally. Horizontal P-values aum to 1.0 State-names are from Breaux, 1977, personal comnunicatlon.

P-value* are exe*noiery, end not baaed on specific data.

FIGURE A. 3 Breaux's Hotel Fire as a Stationary Markov Process

FIRE

BEHAVIOR

OCCUPANT

BEHAVIOR

1 —

REALM \ REALM

x X

/ /

1 s//'

'i L

\ EPISODE^ \ EPISODE

t+1 “ \

7 / /

TIME

FIGURE A. 4 Nelson's "State-Transition" Model of the Building Fire System

AAPPENDIX B: A DESCRIPTION OF THE MARKOV-BASED MODEL

UNDERLYING BFIRES

PART 1: Flow Diagrams Describing Behavioral Subroutines

PART 2 : Contingency Trace Tables and Logical

Decision Flow

B-2

B-21

B—
S r.\KT

B-2
CALL JAMMED: OCC(J) scans

all move alternatives, and

emits any he deems over- crowded

CALL ASSIGN: for OCC(I),

establish probability

values for all move alternatives, and select

one "choice", via Monte

Carlo

CALL REPORT: print-out foT

OCC(I)

- current spatial location;

- prob. values for all move alternatives;

- selected "new" location

for time t+1

CALL UPDATE: I

update location

parameters for next time

frame

<

<

All occupants have been moved

for the current time

frame

I

The desired number of time

frames were run 3Z

>

The desired number of

replications were run

!

BFIKES Executive/ continued

B-3
Flow Diagram for Subroutine INTKPT

B-4
«

OCC(I) is currently

in back- tracking mode

3Z

OCC(I) has reached

his back-track goal

location >

This is OCC (I) *s first move

in back«?tr acking mode

$

Remove OCC (I) from

the back-tracking mode

I Set location coordinates

for t+1 equal to those

for the next receding

time-frame in the

sequence.

r

Flow Diagram for Subroutine BACKUP
START

B-6

9

Flow

Diagram

for

SUBROUTINE

GROUP
OCC Cl) has a "best" exit

'in mind' >

increase the numebr of

occupants with a "best' exit in mind, by 1

Flew Diagram for SUBROUTINE OTHERS
Flow Di

r

OCC(I) is familiar with

no exit; exit #1; or exit

n ~

none

increase the number

of OCC's familiar with

exit #1 by 1

increase the number

of OCC's familiar

with exit #2 by 1

C

*» the number of OCC

in the space

increase the number of

OCC's familiar with no

exit by 1 >

Determine whether at

least 601 of the OCC's

present agree on the

"best" exit

<

I consensus reached for exi

# 1 or exit #2

T

N /

Determine which exit was

agreed upon

agram for SUBROUTINE AGREE

B-8
Flow Diagram for SUBROUTINE ASSIGN
1— —

Determine the cumulative

probabilities of each move alternative confronting

OCC(I) this time frane

5s Generate a Random Number t

s£ -

| Test each m against the selecting t OC

ove alternative I Random Number, J he next move for | C(I)

CALL DOORS2 : If OCC(I) actually

passed through a door, determine

whether he closes it behind him

(for manual doors only)

•S*

Flow Diagram for SUBROUTINE ASSIGN

Continued

B-10
Flow Diagram for SUBROUTINE KPOSS
Flow Di agram for SUBROUTINE EQUAI.^

B- 12
START

K 0

V /

K = K+l

F y— ~ V T MOVE(K) is possible

compute DIST(

from threat t position

K) : distance f o alternative f K j

TOTDST « TOTDST DIST(K)

<Q

total number of

lternative moves (9) T

>

K = 0 31

K = K+i £

^

i . .-^MOVE (K) is possible^-^

PROB(K) = 0 PROB(K) = DIST(K) / TOTDST

<

K * total number of N

alternative moves (9L

0

Flow Diagram for SUBROUTINE TBIAS

B-13
ST \RT

B-14
Flow Diagram for SUBROUTINE EBIAS , continued

B-15
iZ_ T <

Threat location is known to OCC(I)

Flow Diagram for SUBROUTINE EVAL8
Flow Diagram for SUBROUTINE FVAL20
Flow Diagram for SUBROUTINE D00RS1

B-18
k

OCC (I) ' s move wi

be thru a door

Door is of the \T

automat ic- closing

type

CALL RANDOM: generate

a random number.

Probabilistically

determine whether

OCC(I) closes the

door behind him.

Flow Diagram for SUBROUTINE D00RS2

B-19
START

Flow Diagram for SUBROUTINE JAMMED

B-20
Contingency Trace for BDGFIRE/LEVEL 1 EXECUTIVE PROGRAM

Contingencies

1 2 3 4 5

OCC(I) is interrupted during the current time frame T F F F F

OCC(I) is already in a helping mode T F F F

OCC(T) confronts an injured or handicapped other during the current time frame

F T T

OCC(I) first enters a helping mode,

during the current time frame T F

Outcomes

1 2 3 4 5

Next action is determined by the interruption mode in effect

*

Next action is' determined by the

helping mode in effect

ft *

Next action reflects either goalseeking or threat-evas ion

depending upon OCC(I)'s current

status evaluation

* *

Contingency Trace for SUBROUTINE INTRPT

Contingencies 1 2 3 4 5

OCC(I) is currently in the back- tracking mode T F F F F

The number of interruptions encountered by

OCC(I) is less than his interruption limit T T T F

Back-tracking is probabilistically initiated T F F

Interruption is probabilistically initiated T F

Outcomes

1 2 3 4 5

Exit INTRPT

Enter back-tracking mode

•

* ...

Enter interruption mode *

No interruption to OCC(I) during this time- frame * •

B-21
Contingency Trace for SUBROUTINE BACKUP

Contingencies 1 2 3

OCC(I) is currently in the back-tracking mode

OCC(I) has reached his back-track goal

F T

F

T

T

Outcomes

1 2 3

This is OCC(l) 's first move in the back-track mode *

Set location coordinates for time-frame t+1 equal to those of the next receding time-frame in the sequence

Remove OCC(I) from the back- tracking mode

* •

*

Contingency Trace for SUBROUTINE GROUP

Contingencies

1 2 3 4 5 OCC(I) has a "best" exit 'in mind' T T T F F There are no other occupants in the space T F F T F

No other occupants have a "best" exit T F

Outcomes

1 2 3 4 5 Return * * *

Determine whether an exit consensus was reached * _J

*

Contingency Trace for SUBROUTINE OTHERS

Contingencies

Possible

Situations

1 2 3 4 More than one occupant in the space F T T T

OCC(I) is either injured or handicapped T T F OCC(I) has a "best" exit 'in mind' T F T

Outcomes

1 2 3 4

Return *

Increase number of injured/handicapped by 1 * ft

Increase number of occupants with exit in mind

by 1 * ft

B-22
Contingency Trace for SUBROUTINE AGREE

Possible Situations

1 2 3 1 2 3 For Each Occupant:

OCC(I) is familiar with Exit #1 T F F OCC(I) is familiar with Exit 12 F T F OCC(I) is familiar with no exit F F T For entire group:

Consensus reached for Exit *1 T F F Consensus reached for Exit #2 F T F No con ensus reached F F T

Outcomes

1 2 3 1 2 3 Increase # of occ's familiar with

exit #1 by 1 *

Increase 1 of occ's familiar with

exit 12 by 1

*

Increase # of occ's familiar with

no exit by 1 *

Record consensus ft ft ft

Contingency Trace for SUBROUTINE ASSIGN

Contingencies

Possible Situations

1 2 3 4 5 6 7 8 OCC(I) is handicapped/injured T T F F F F F F time ODD F T

- OCC(I) was interrupted during t T F F F F F

OCC(I) helps handicapped other T F F F F OCC(I) makes positive status eva: T T F F An exit was agreed upon T F T F

Outcomes

1 2 3 4 5 6 7 8 Return ft

OCC(I) remains in place ft

Bias toward helping other ft

Bias toward exit-seeking ft ft

Bias toward threat-evasion ft

Equalize choice prob's (confuse) ft

B-23
Contingency Trace for SUBROUTINE KPOSS

Cont ingenc ies

1 2 3

A phy s ical - envi ronmental barrier (e.g., a wall)

has been encountered F F T The location denoted by the move alternative

under consideration is perceived as over- crowded F T T

Outcomes

1 2 3

The move alternative under consideration is possible, and its probability of selection

will be assigned *

The move alternative under consideration is not possible, and its probability of selection is 0.000 * ft

Contingency Trace for SUBROUTINE EQUALZ

Contingencies

1 2 MOVE(K) is possible, for OCC(I), during

time frame t T F

Outcomes

1 2 PROB(K) = 1 / the number of possible move alternatives available to OCC(I)

*

PROB(K) = 0 *

Contingency Trace for SUBROUTINE TBIAS

Cont ingency

1 2

Move K is possible F T

Outcomes

1 2

The probability of selecting move K is 0.000 ft

The probability of selecting move K is a function of DIST(K): the distance between the

threat and location K. That is, as DIST(K)

increases, Prob(K) also increases *

B-24
Contingency Trace for SUBROUTINE EBIAS

Cont ingenc ies

1 2 3 4

Move K is possible F T T T

The distance between a goal and OCC(I)

[ DI ST ( K) ] » 0; i.e., move alternative K

is into an exit F T T More than one move alternative is into an exit F T

Outcomes1 2 3 4

The probability of selecting move K is 0.000 *

The probability of selecting move K is a function of DIST(K), such that as DIST(K)

decreases, Prob (K) increases *

The probability of selecting move K is 1.000 A

bach of the exit alternatives has an equal chance

of selection A

Cont ingency < mce for SUBROUTINE EVAL8

Cont i ngenc u- ,

1 2 3 4 S 6 7

The Threat location is known T T T F F F T Some exit is ..greed - upon T T F T T F F The distance between OCC(I) and the

threat inc leased between t-1 and t F T The distance between OCCfl) and the exit goal decreased between t-1 and t T F The distance between OCCfl) and the

exit goal decreased and the distance

between OCi ( 1 ) and the treat increased

between t 1 and t T F

Outcomes

1 2 3 4 5 6 7

Status evaluation is positive * * A

Status evaluation is negative A * A A

B-25
Contingency Trace for SUBROUTINE F.VAL20

Contingencies 1 2 3 4 S 6 7

Some exit is agreed-upon T T T T F F F

The Threat location is known T T F F T T F

Actual distance from 0CC(1) to the exit is greater than his

maximum allowable distance T F T F

Actual distance from 0CC(1) to the threat is greater than his

maximum allowable distance T F

Outcomes

1 2 3 4 5 6 7

Status evaluation is positive ft ft ft

Status evaluation is negative * ft ft ft

Contingency Trace for SUBROUTINE JAMMED

Cont ingenc ies

1 2 3 4

The location denoted by alternative move K is currently occupied by one or more other

individuals F T T T Alternative move K is into an exit T F F The location denoted by alternative move K is perceived by OCC(I) as overcrowded T F

Outcomes

1 2 3 4

The location denoted by alternative move K is judged by OCC(I) to be enterable ft ft ft

The location denoted by alternative move K is judged by OCC(I) to be non- enterable ft

B-26
Contingency Trace for SUBROUT I RE DOORS1

Contingencies

1 2 3 4

A door is encountered by OCC(I) during t F T T T

This door is already open T F P

A closed door is left closed T F

Outcomes

1 2 3 4

Bypass door routines *

Consider movement thru the door as a move alternative for OCC(I), during t

* ft

Delete the thru-door move from the array

of move alternatives foT OCC(I), during

t

ft

Contingency Trace for SUBROUTINE DOORS2

Contingencies

1 2 3 4

OCC(I)'s move will be through a door,

during t F T T T The door is of the automatically-closing

type T F T As OCC(I) passes thru the door, he closes

it behind him T F

Outcomes

1 2 3 4

Bypass door routines ft

Update status of DOOR(N) , showing that

it is now closed ft ft

Update status of DOOR(N) , showing that

it is now open ft

B-27

APPENDIX C FORTRAN-V LISTING OF THE BFIRES SIMULATION PROGRAM

C-l
S56ATCH

C

C>MoMOMoK**>toM:>K**>K>K>W<>|:W'*>K>toM<>MoloK>K*>K:K>totc>fc>K*:>t:*>K*>K*>^:^ c

C "SIMULATING HUMAN BEHAVIOR IN FIRES"

C

C COMPUTER SIMULATION PACKAGE WRITTEN BY:

C FRED i. STAHL. RESEARCH ARCHITECT

C ARCHITECTURAL RESEARCH SECTION

C CENTER FOR BUILDING TECHNOLOGY

C NATIONAL BUREAU OF STANDARDS

C

C PREPARED FOR:

C DESIGN CONCEPTS PROGRAM

C CENTER FOR FIRE RESEARCH

C NATIONAL BUREAU OF STANDARDS

C

C**oWo|o)ofc*>loR*>!o:;>!o|:>Mo!o|3|c***>k>k*?:o^

C

C UPDATED: 23 NOVEMBER 197?

C

C CHECKED: 23 NOVEMBER- 197?

C

C

c

C: BUILDING FIRE: LEVEL 2.8 — BDGF IRE : LEVEL23 — BFIRES

C

C: SIMULATING HUMAN BEHAVIOR IN BUILDING FIRES

CC

DIMENSION I TYPE 1(20) . ITYFE2C20) . IDPASS (20)

DIMENSION IXTRC'E (20, 30) . I YTRCE (20. 30)

DIMENSION I BACK (20) , JT I ME (20) . IN ITYO (20) . INITXO (20)

DIMENSION INTR (20) . INTNUM(20)

DIMENSION IFMr(2O),JFMT(20).KFMT(20). I ENTER (9) D I MENS I ON I BAR ( 20 ,20,2). LBYSTD ( 20 ) , I HAND 1(20), KNOUAY (20)

DIMENSION. I NTL I M (20) . IBYSTDC20) ,NE(2G) ,NPOINT(20)

DIMENSION PTD I ST (20) .FED I ST (20),A(9),M(9),DIST(9),P(9)

DIMENSION IGOALX(20, 10) , !GOALY(20, 10) ,KXO(20) ,KYO(20)

DIMENSION POPEN (20) , PCLOSE (20) , I DOOR (30, 4) . ID0PEN(30.30)

INTEGER XT, YT,X0(2B) , YOC20) ,XE( 10) , YE( 10) , TOTBAR, TOTIME

INTEGER XLO (20) , XH 1(20), YLO (20) , YH I (20) , EVLOPT

INTEGER X0B(20, 30). Y03(20. 3D). REPORT

C

READ (5,101) IFMT

READ (5,103) JFMT

READ (5,104) KFMT

C

C: INITIALIZE THE SIMULATION...

C: (1) ENVIRONMENTAL PARAMETERS:

READ (5, 100; XT, YT, NUMEXT , MXT I ME , NSPACE . EVLOPT, MK . C

C-2
1 IALLOU,ND, REPORT

READ <5. IFMT) (XE(I) , I-1,NUMZXT) , (YEC I) , I-l.NUMEXT)

DO 10 IS-1,NSPACE

READ (5, IFMT) NE( IS) ,NPOINT( IS)

NEXIT**NE( IS)

TOTBAR eNPO I NT ( I S

READ (5, JFMT) ( IBAR ( IS, I , 1 ) ,

I - 1 , TOTBAR)

READ (5, JFMT) ( IBAR (IS, 1 ,2) , 1 = 1 , TOTBP.R)

READ (5, IFMT) ( IGOALXUS, JEXIT) , JEXIT- 1,NUMEXT)

1 ( IGOALY( IS, JEXIT) . JEXIT- 1,NUMEXT)

10 READ (5, IFMT) XLO( IS) ,XHI ( IS) , YLO( IS) , YHI (IS) * READ (5, JFMT) ( IDOOR( I, 1) , I»1,ND)

READ (5, JFMT) ( I DOOR ( 1,2) ,

I -1 ,ND)

READ (5, JFMT) < IDOOR( 1 ,3) ,

I - 1 ,ND)

READ (5, JFMT) (IDOOR(I,4>, I-1,ND)

C: (2) SYSTEM PARAMETERS:

READ (5,102) NUMOCC,TDTIME,IRAND,NUMREP,PI2,PI0

DO 40 I -1 ,NUM0CC

IBYSTDC I) =0

40 CONTINUE

C: (3) OCCUPANT PARAMETERS:

DO 45 N=! ,NUMOCC

READ (5,KFMT) INTLIM(N) ,LBYSTD(N) , IHANDI (N) ,KNOUAYCN) ,XO(N) ,YO(N)

1 ,PUPEN(N),PCLOSE(N)

KXO(N) =XO(N)

KYG(N) =YO(N)

INITXC(N) =XO(N)

INITYC'(M) °YO(N)

45 CONTINUE

C

C ** EXECUTE THE SIMULATION EXPERIMENT ** BFIRES EXECUTIVE **

C

C: RUN THE DESIRED NUM3ER OF REPLICATIONS:

C

DO 90 111=1, N'JMREP

DO 91 N=1,NUM0CC

XO(N)-KXOCN)

91 YO(N) -KYO(N)

C

DO 92 N7HIS=1,NUM0CC

IBACK(NTHIS) -0

JTIME(NTHIS) *0

INTR(NTHIS) =0

INTNUM(NTHIS) *0

ITYPE1 (NTHIS) =0

ITYPE2CNTHIS) =0

92 IDPASS(NTHIS) =0

C

C: RUN THE SIMULATION FOR THE DESIRED NUMBER OF TIME UNITS:

C

DO 50 ITIME-l.TOTIME

C

DO 501 I=1,ND

501 I DOPES' ' I TIME) =IDOOR( 1,4)

C-3
c

C: FOR EACH . ..£ INCREMENT, RUN ALL OCCUPANTS IN THE SPACE...

DO 60 NTHIS-1 .NUMOCC

IXTRCE(NTHIS, ITIME) -XO(NTHIS)

IYTRCECNTHIS, ITIME) -YO(NTHIS)

XOB(NTHIS, ITIME) -XO (NTH IS)

YOB(NTHIS, ITIME) -VO(NTHIS)

N-0

15 N-N+l

IF ( ( (XLO(N) .LT.XO(NTHIS) ) .AND.

1 (\LO(N) .LT. YO(NTHIS) ) ) .AND.

2 ( 0<HI (N) .GT.XO(NTHIS) ) .AND.

3 (YHI (N) .GT. YO(NTHIS) ) ) ) GO TO 25

GO TO 20

25 IS-N

GO TO 26

20 IF (N.LT.NSPACE) GO TO 15

IS-NSPACE

26 TOTBAR-NPO INT (IS)

CALL INTRPT ( ITIME. NTHIS, IHANDI, INT, IBYSTD, IEVAL,

1 XO, YO. I BAR , TOTBAR » XT, YT, NAGREE , XE » YE , IAGREE.

2 IRAND.P.MOVE.XK, YK.K.L. IS, IGOALX. IGOALY, I ENTER.

3 X, INTLIM, INTR, INTNUM.PI2.PI0)

IF ( INT.EO. 1) GO TO 2?

IF (INT.EQ.2) GO TO 30

GO TO 31

27 I TYPE 1 (NTHIS) ®I TYPE 1 (NTH IS) + 1 GO TO 70

30 CALL BACKUP ( IBACK.XO, YO, INITXO, INITYO.XOB, YOB.

1 ITIME, NTH IS, NEUXO.NEIJYO, INTR, JTIME)

I TYPE2 ( NTH I S ) - 1 TYPE2 (NTH I S ) +

IF (INTR (NTH IS) .EQ.0) GO TO 31

GO TO 71

31 CONTINUE

IF (IBYSTD (NTH IS). EQ.l) GO TO 70

CALL GROUP (NTHIS.NUMOCC, I HAND I , KNOUAY» KOOCC . NHAND I , NKNOU, NAGREE

1 IAGREE)

IF (NHANDI.GT.0) GO TO 65

GO TO 67

65 CALL BYSTND ( IBYSTD. NTH IS) IF (IBYSTD (NTH IS). EQ.l) GO TO 70

67 IF (EVLOPT-1) 66,68,69

68 CALL EVAL8(X0,Y0.XT,YT,XE. YE, NTHIS. IAGREE. ITIME, IEVAL.

1 PTD I ST, TD I ST, FED I ST, ED I ST, IS, IGOALX, IGOALY)

GO TO 70

69 CALL EVAL20 (MXTIME.MK.XO.YO.XE, YE. NTHIS, IAGREE.

1 ITIME. C, IEVAL)

70 CALL JAMMED ( ITIME. NTHIS, IHANDI , INT, IBYSTD, IEVAL.

1 XO, YO, I BAR , TOTBAR , XT, YT, NAGREE » XE , YE . IAGREE. I RAND.

2 P.MOVE.XK, YK,K, IALLOU.NUMOCC, I ENTER)

CALL ASSIGN ( ITIME, NTH IS, IHANDI, INT. IBYSTD, IEVAL.

1 XO, YO, IBAR, TOTBAR. XT, YT, NAGREE, XE, YE. IAGREE,

2 IRAND.P.MOVE.XK.YK.K.L, IS. IGOALX. IGOALY, I ENTER.

3 X. IDOOR.POPEN.ND.MDOOR.PCLOSE)

C-4
CALL NEUXY ( IT I ME -NTH IS. I HAND I, INT. IBYSTD. IEVAL.

1 XO.YO. IBAR.TOTBAR. XT, YT, NAGREE. XE. YE, I AGREE.

2 I RAND . P . MOVE , XK , YK . K . NEUXO . NEIJYO

CALL PASSG ( IDPASS. I DOOR. XO.YO. NTH I S.ND. NEUXO. NEUYQ)

71 IF (REPORT. EQ. 1) GO TO 72

GO TO 61

72 CALL REPORT (I TIME. NTH IS. I HAND I, INT. IBYSTD, IEVAL.

1 XO , YO , I BAR . TOTBAR . XT. YT. NAGREE . XE . YE , I AGREE

2 IRAND.P.MOVE.XK.YK.K, NUMEXT, NUMOCC . TOT I ME , INTLIM,

3 LBYSTD , KKOUAY, PTDIST.TDI ST. PED I ST , ED I ST. NEUXO . NEIJYO.

4 EVLOPT, IDOOR. IDOPEN.ND, INTR)

61 CALL UPDATE ^XO.YO, NTH IS, NELJXO, NE'JYO)

60 CONTINUE

50 CONTINUE

CALL TRACE ( I XTRCE , I YTRCE , NTH I S , I T I ME , NUMOCC , TOT I ME

CALL TOTALS ( IDPASS. I TYPE 1 . ITYPE2. NTH IS, NUMOCC)

90 CONTINUE

C

C: INPUT FORMATING

C

100 FORMAT (5(12, IX), 2(11, IX), FI. 0, IX, 12, IX. 12. IX, ID

101 FORMAT (20A4)

102 FORMAT (2(12, IX), 15. IX, 12. 2( 1X.F4.2)

103 FORMAT (20A4)

104 FORMAT (20A4)

END

C

C

c

cc SUBROUTINE

SUBROUTINES GO HERE //////////////////////

BYSTND (IBYSTD.NTHIS)

IBYSTD (NTH IS) = 0

RETURN

END

C

Cc SUBROUTINE GROL'o ( NTH I S . NUMOCC , I HAND I , KNOUAY. KQOCC , NHAND I

1 NKNOU, NAGREE, IAGREE)

DIMENSION KN0UAY(20)

CALL OTHERS (NTHIS, NUMOCC. I HAND I , KNOUAY, KOOCC, NHAND I , NKNOIJ)

IF (KNOUAY (NTH IS) .GT.0) GO TO 1 IF (KOOCC. EQ.0) GO TO 999

GO TO 2

1 IF (KOOCC. EG. 0) GO TO 999

IF ( NKNOU. EO.0) GO TO 999

2 CALL AGREE (NTHIS. NUMOCC. IHANDI .KNOUAY.KOOCC.NHANDI.

1 NKNOU, NAGREE. IAGREE)

999 RETURN

END

C

cc

05
non

SUBROIT *E OTHERS ( NTH I S . NUMOCC » I HAND I , KNOUAY. KOOCC . NHAND I #

1 NKNu.

DIMENSION I HAND ! ( 20 ) # KNOUAY C 20

NKNOU=0

NHAND 1=0

IF (NUMOCC.GT. 1) GO TO 1 KOOCC=0

GO TO 999

1 KOOCC =

DO 50 I-l.NUMOCC

IF (I. EG. NTH IS) GO TO 50

IF ( I HAND I ( I) .EQ. 1) GO TO 51

GO TO 50

51

NHAND I -NHAND 1+1

50 CONTINUE

DO 60 1 = 1. NUMOCC

IF (I.EO.NTHIS) GO TO 60

IF (KNOUAY(I) .GT.0) GO TO 61

GO TO 60

61 NkNOU-NUNOIJ+1

60 CONTINUE

999 RETURN

END

SUBROUTINE AGREE ( NTH IS. NUMOCC. IHANDI.KNOLJAY. KOOCC.

1 NHANDI.NKNCU.NAGREE. I AGREE)

DIMENSION KNOUAYC20)

KONE=0

KTUO=0

K2ERO=0

DO 50 1=1. NUMOCC

IF (KNCIJAYC I ) . EQ . 1 ) GO TO 51

IF (KNQUAYC I ) . EQ . 2) GO TO 52

IF (KKCUAYU) .EQ.0) GO TO 53

GO TO 50

51 K0NE=K0NE+1

GO TO 50

52 KTU0=KTlJ0-:-1

GO TO 50

53 KZERO-ICZERO+1

50 CONTINUE

ONE=KC'NE

TUO =KTUO

ZERO=KZERO

SUM= 0 N E +TUO+ZE R 0

PSUM=.60*SUM

IF ((ONE.GE.PSUM) .OR. (TUO.GE.PSUM)) GO TO 1 NAGREE=0

IAGREE=0

GO TO 2

1 NAGRGE=

IF (ONE.GE.PSUM) GO TO 3

C-6
o

no

n

n

ooo

row

1 AGREE =2

GO TO 2

I AGREE C 1 RETURN

END

SUBROUTINE ASSIGN ( ITIME.NTHIS# I HAND I, I NT, IBYSTD# 1EVAL#

1 XO.YO. IBAR . TOTBAR. XT. YT# NAGREE. XE. YE# I AGREE#

2 IRRND.P.MOVE.XK.YK.K.L# IS# IGOALX# IGOALY, I ENTER.

3 X, IDOCR#POPEN.ND#MDOOR#PCLOSE)

: THIS SECTION OF "ASSIGN" DETERMINES WHETHER ITIME (THE

: CURRENT VALUE OF TIME) IS ODD OR EVEN. INJURED OR

: HANDICAPPED OTHERS MAY MOVE ONLY ON ODD TIME VALUES:

DIMENSION IHANDI (20) # IBYSTD(20) #P(9) , IMPOSSO) #M(9)#

I A(9)#DIST(9) #CUM(9)

INTEGER XO(20)#YO(20),XE(10)#YE(I0)#XT#YT

T I ME = I T I ME

AT I ME «=T I ME/2.

JT I ME =ATI ME

BTIME“JTIME

TEST=ATIME-BTIME

C

C: NOW# RUN THROUGH THE "ASSIGN" SUBROUTINE# TO SET MOVE

C: PROBABILITY VALUES ACCORDING TO THE APPROPRIATE BIASES.

C: SPECIFIC BIASING ROUTINES ARE INCLUDED AS SEPARATE SUBC: ROUTINES.

C

IF ( IHANDI (NTH IS). EQ.l) GO TO 1 GO TO 11

1 IF (TEST.NE.0. ) GO TO 2

11 IF (INT.EQ.l) GO TO 2

IF (IBYSTD(NTHIS) .EQ.l) GO TO 3

IF (IEVAL.EQ.l) GO TO 4

IF ( (XT.EQ.0) .AND. (YT.EQ.0) ) GO TO 5

IF (NAGREE.EQ.0) GO TO 434

GO TO 504

404 CALL TBIAS ( ITIME.NTHIS# IHANDI# INT# IBYSTD# IEVAL#

1 XO.YO. I BAR# TOTBAR , XT, YT# NAGREE# XE# YE # I AGREE#

2 IRAND,P,MOVE#XK.YK#K#L# IS. IGOALX, IGOALY, IENTER)

GO TO 6

4 IF (NAGREE. EO.0) GO TO 404

504 CALL EBIA3 ( ITIME.NTHIS, IHANDI# INT. IBYSTD# IEVAL#

1 XO.YO# IBAR,T0TB.}R#XT# YT. NAGREE #XE. YE# IAGREE,

2 IRAND.P.MOVE.XK, YK.K.L, IS# IGOALX, IGOALY# IENTER)

GO TO 6

2 P(5) “1 .0

DO 50 K«l#4

50 P(K) *0.0

DO 51 K-6,9

51 P(K)

C-7
GO TO 1 3 CALL h. .AS ( ITIME.NTHIS, IHANDI » I NT, IBYSTD. IEVAL,

1 XO. VO, IBAR, TOTBAR, XT, YT. NAGREE. XE. YE, IAGREE.

2 I RAND, P)

CO TO 6

5 IF (NAGREE.NE.0) GO TO 504

CALL EQUAL2 ( I TIME, NTH IS, I HAND I, I NT, IBYSTD, IEVAL,

1 XO, YD, I BAR , TOTBAR , XT, YT, NAGREE, XE , YE , IAGREE,

2 I RAND , P , MGVE . XK , YK » K . L . IS, IGOALX, IGOALY, IENTER)

6 CALL DOORS 1 ( ITIME,NTH1S, IHANDI, INI, IBYSTD. IEVAL,

1 XO.YO. I PAR, TOTBAR, XT. YT, NAGREE. XE, YE. IAGREE.

2 IRA: ;D, P, MOVE, Xl(, YK» K, L, IS, IGOALX. IGOALY. IENTER.

3 X, IDOOR.POFEN, ND, MDOOR, PCLCSE, IX)

601 CONTINUE

DO ICO I>1,9

IF (K.EG. 1) GO TO 200

CUMd()=P(K)+CUM(K-l)

GO TO 1PC

203 CUM dO =P GO

103 CONTINUE

K=G

CALL RANDOM ( ITIME.NTHIS, IHANDI, INT, IBYSTD. IEVAL,

1 XO.YO. IBAR, TOTBAR, XT, YT. NAGREE. XE. YE. IAGREE, IRAND.

2 P , MOVE , XK , YK , K , L , IS, IGOALX. IGOALY, IENTER, X)

7 K=K+1

IF (X) 71,72,72

71 X=X'i;( - 1

72 RAND=CUNGO-X

IF (RAND) 52,53.53

53 MOVE=K

GO TO 8

52 IF dOLT.9) GO TO 7

B CALL D00RS2C ITIME.NTHIS, IHANDI, INT, IBYSTD. IEVAL.

1 XO.YO, I BAR, TOTBAR, XT, YT, NAGREE. XE, YE. IAGREE.

2 IRAND, P, MOVE, XK.YK.K.L, IS, IGOALX. IGOALY. IENTER.

3 >1. I DOOR, POPEN.ND, MDOOR, F'CLOSE. IX)

12 RETURN

END

CC

C SUBROUTINE KPOSS ( ITIME, NTH IS, IHAND I , INT, IBYSTD. IEVAL.

1 XO.YO, I BAR, TOTBAR, XT, YT. NAGREE, XE, YE. IAGREE, IRAND,

2 P, MOVE. XK.YK.K.L, IS, IGOALX, IGOALY. IENTER)

DIMENSION I BAR (20,20,2) . IENTER (9)

INTEGER XO ( 20 ) , YO ( 20 > , Xl< , YK . TOTBAR

CA-L KTOXY ( I TIME, NTH IS, IHANDI, INT, IBYSTD, IEVAL.

1 XO.YO, IBAR, TOTBAR, XT, YT, NAGREE, XE, YE, IAGREE, IRAND,

2 P.MOVE.XK.YK.K)

ICROSS = (XO (NTH IS) +XK) /2

JCROSS = ( YO ( NTH I S ) +YK ) /2

1=0

J=1

1 1=1+1

C-8
oono

—

vd

cd

-vj

cd

(_n

x\

oj

ro

—

or»n

IF ( I . GT. TOTBAR) GO TO 5

IF <IBAR(IS,I.J).EQ.ICROSS) GO TO 2

GO TO 1 2 J=J+1

IF (IBARCIS, I,J).EO.JCROSS) GO TO 4

J-J-l

GO TO 1 4 CONTINUE

L=0

GO TO 6

5 L = 1 IF ( I ENTER (IO .EQ.0) GO TO 4

6 RETURN

ENT

SUBROUTINE KTOXY( ITIME,NTHIS, IHAND I . INT, IBYSTD. IEVAL,

1 XO,Y0, IBAR,T0T6AR,XT,YT,MAGREE,XE.YE, IAGREE. IRAND.

2 REMOVE, XK.YK.K)

INTEGER XO(20),YO(20),XK,YI<

GO TO (1, 2, 3, 4,5, S. 7, 3, 9),!C

XK=XQ (NTH IS) -2

YI(=Y0 (NTH IS) -2

GO TO 10

XK=XCl(N7HIS)-2

YK=YG(NTHIS)

GO TO 10

XK=XO (NTH IS) -2

YIOYO (NTH IS) +2

GO TO 10

XK=XO (NTH IS)

YI(=YO (NTH IS) -2

GO TO 1C

Xk=XO(NTHIS)

YI<=YC (NTH IS)

GO TO 10

XK=XO (NTH IS)

YK=YO (NTH IS) +2

GO TO 10

XK=XO (NTH IS) +2

YK=Y0(NTHIS)-2

GO TO ?0

XK=XG(NTKIS)+2

YOYO (NTH IS)

GO TO 10

X1(=X0 (NTH IS) +2

YK = YO ( NTH I

1 +2

RETURN

END

: RANDOM NU ~R GENERATOR:

09
(—)(—)0

fi.cn

oj

to

ooooo

ooo

cr>

cn

ooooo

C

SUBROU AE RAND0M( ITIME.NTHIS, IHANDI, INT. IBYSTD. IEVAL.

1 XO. VO, IBAR.TOTBAR.XT.YT.NAGREE.XE.YE, IAGREE. IRAND,

2 P.MOVE.XK, YK.K.L, IS. IGOALX, IGOALY. I ENTER. X)

: IBM UNIFORMLY DISTRIBUTED RANDOM NUMBER GENERATOR:

: SUBROUTINE "RANDU" (FROM SSP-2 PACKAGE). BASED ON THE

: POUER-RESIDUE METHOD.

IY=IRAND*65539

IF (IY) 5,6,6

I Y= I Y*2 147433647*1

X= I

X=X*. 4S56S 13E-9

I RAND- 1

RETURN

END

SUBROUTINE E0UA1Z ( I TIME, NTH IS. IHANDI. INT. IBYSTD, IEVAL.

1 XO.YO, IEAR.TCTSAR.XT, YT.NAGREE.XE. YE. I AGREE,

2 ! RAND, P. MOVE, XK.YK.K.L, IS. IGOALX, IGOALY.

El ITER)

: "EBt'ALZ n (EQUALIZE) CAUSES THE PROBAB T LITY VALUES OF THE

: VARIOUS POSSIBLE MOVES TO BE SET EQUAL TO EACH OTHER. RESULTING IN

: NO BIASING EFFECT:

INTEGER X0(20) ,Y0(20) .XK.YK

DIMENSION IMPOSS (S). P (9)

NUN?0S=0

DO 1 K = 1 ,

CALL KPOSS (I TIME. NTH IS, I HAND!. INT. IBYSTD. IEVAL.

1 XO.YO, IBAR.TOTBAR.XT.YT.NAGREE.XE.YE. I AGREE,

2 I RAND. P. MOVE, XK.YK.K.L. IS, IGOALX. IGOALY. I ENTER)

IF (L.EO. 1) GO TO 2

GO TO 3

nl'mpos=numpos+i

IMPOSS (K) =0

GO TO 1 IMPOSS (K) =1

CONTINUE

DO 4 K= 1 ,9

IF ( IMPOSS CIO .EQ. 1) GO TO 5

POO =1 . 0/FLOAT ( NUMPClS

GO TO 4

P CIO =0. G

CONTINUE

RETURN

END

SUBROUTINE TBIAS ( ITIME.NTHIS. IHANDI, INT, IBYSTD. IEVAL.

C-10
I XO.YO, 1 BAR, TOTBAR. XT, YT.NAGREE.XE. YE, I AGREE,

1 IRAND. P. MOVE. XK, YK. K, L, IS, IGOALX, IGOALY, IENTER)

C

C: # TB I A3 " (THREAT-BIAS) CAUSES THE PROBABILITY VALUES OF THE

C: VARIOUS MOVES TO BE ADJUSTED SO AS TO BIAS TOWARD THREATS': REDUCTION (I.E., BIASING TOWARD INCREASING THE

C: DISTANCE BETWEEN OCC(NTHIS) AND THE THREAT POINT):

C

INTEGER XO ( 20 > , YO ( 20 ) , XT. YT, XK , YK

DIMENSION M(9) ,DIST(9) ,P(9)

TOTDST=0

C

C: FOR EACH POSSIBLE MOVE, COMPUTE DISTANCE TO THREAT POINT:

C

DO 10 K=l,9

CALL KF'OSS ( I TIME, NTH IS. IHANDI, I NT. IBYSTD, IEVAL,

1 XO.YO, IBAR,TOTBAR,XT, YT.NAGREE.XE. YE, I AGREE.

2 IRAND, P, MOVE, XK,YK,K,L, IS, IGOALX, IGOALY, IENTER)

IF (L.EQ. 1) GO TO 1 MOO = 1 GO TO 10

1 MOO =0

DISTOO =S9RT(FLCAT((XT-XK>**2+<YT-YK)**2)

TOTDST=TOTDST+D 1ST CIO

10 CONTINUE

C

C: FOR EACH PCSSIBLE MOVE, COMPUTE THE MOVE-PROB., PCK):

C

DO 15 K=1 ,9

IF (MOO.EQ.0) GO TO 2

P00=0.

GO TO 15

2 POO ~D I ST 00 /TOTDST

15 CONTINUE

RETURN

END

C

C

C

SUEROUT I NE EB I AS ( I T I ME . NTH I S . I HAND I , I NT, I BYSTD . I EVAL

1 XO.YO, IBAR.TOTBAR, XT, YT.NAGREE.XE. YE, I AGREE.

2 I RAND . P , MOVE , XK , YK . K , L . IS, IGOALX, IGOALY, IENTER)

C

C: THE DISTANCE BETWEEN OCC(NTHIS) AND THE AGREED: UPON EXIT:

C

INTEGER XO(20) . YOC20) ,XE( 10) ,YE( 10) .XK.YK

DIMENSION M(9),DIST(9),XE(10),YE(10),A(9),P(9)

DIMENSION IGOALXC20. 10) . IGOALY(20, 10)

TOTDST=0

SUMA=0

C

C: FOR EACH POSSIELE MOVE, COMPUTE THE DISTANCE TO THE

C: AGREED-UPON EXIT:

C

C-ll
non

iv'

-m

-\i

ro

cn

<-n

oj

DO 10 ’ ,9

CALL Kt ,oS (ITIME.NTHIS. IHANDI, INT. IBYSTD, IEVAL.

1 XO.YQ, IBAR. TOTBAR, XT, YT, NAGREE. XE. YE, I AGREE,

2 I RAND

P . MOVE . XK , YK . K , L . IS. 1G0ALX, IGOALY. I ENTER)

IF (L.EQ. 1) GO TO 1 MOO = 1 GO TO 10

1 MOO =0

D ISTOO =SQRTC FLOAT (

IGOALXC IS. I AGREE) -XK)**2+

1 CIGOALYCIS, I AGREE ) - YK ) **2 )

TOTDST=TOTDST+D 1ST (K)

10 CONTINUE

C

C: FOR EACH POSSIBLE MOVE, COMPUTE MOVE-PROB. VALUES, PCK)

C: IF D ISTOO =0. 0. MOVE K IS SELECTED. IF DISTCK)=G.O FOR

C: MORE THAN ONE MOVE ALTERNATIVE, THEN THE M0VE-PR0B3 . FOR

C: THESE ARE EQUALIZED:

C DO 15 K = 1 .

IF CMCIO .EQ. 1) GO TO 15

IF CD 1ST CIO . EQ . 0 . ) GO TO 15

A CIO =TOTDST/D 1ST (K)

SUMA=SUMA+A CiO

15 CONTINUE

C

K=0

IC=!C-:-l IF CMCIO .EQ. 1) GO TO 3

IF CD 1ST CIO . EQ . O . ) GO TO 5

PCIO -ACIO/SUMA

GO TO 4

PLj -0.

IF CN.LT.9) GO TO 2

RETURN

ZEF 0-0

DO 20 |>!,9

IF CMCIO .EQ. 1) GO TO 20

IF 'DIET CIO . EQ.0. ) GO TO S

GO TO 20

ZER0=ZER0+1

0 CONTINUE

DO 25 101,9

IF CMCIO .EQ. 1) GO TO 707

IF CD ISTCIO . EQ . 0 . ) GO TO 7

07 Pi. 10=0.0

GO TO 25

PCIO -1. /ZERO

5 CONTINUE

RETURN

END

SUBROUTINE EVAL (XO, YQ.XT, YT.XE, YE.NTHIS, I AGREE.

C-12
noon

o

non

nnn

Xi.ro

1 ITIME. IE VAL, PTD 1ST . TD 1ST, RED 1ST. ED 1ST,

2 IS. IGOALX. 1G0ALY)

INTEGER XO(20).YO(20),XE( 18) , YE ( 10) ,XT. YT

DIMENSION PTDISTC20) . IGOALX(20. 10) . IGOALY(20, 10)

DIMENSION RED 1ST (20)

IF ( (XT.GT.0) .AND. (YT.GT.0) ) GO TO 1 GO TO 3

1 TDIST=SQRT(FL0AT((X0(NTHIS)-XT)**2+(Y0(NTHIS)-YT)**2))

IF (ITIME. GT. 1) GO TO 5

PTD 1ST (NTH IS) =TD 1ST

5 TCHANG=TD IST-PTD 1ST (NTH IS)

IF ( IAGREE . EQ . 0) GO TO 2

ED 1ST=SClRT (FLOAT ( ( XO ( NTH I S

) - 1 GOALX ( IS. I AGREE) )**2 +

1 ( YO (NTH IS) - IGOALY( IS. I AGREE) )**2)

IF (ITIME. GT.l) GO TO 6

FED 1ST (NTH IS) =ED 1ST

6 ECHANG=ED IST-FED 1ST (NTH IS)

IF ( (TCKRNG. GE . 0. ) . AND. (ECHANG . LE . 0 . ) ) GO TO 2

3 IEVAL c O

GO TO 4

I E VAL = 1 PTD i ST ( NTH I S ) =TP I ST

PED 1ST (NTH IS) =ED 1ST

RETURN

END

SUBROUTINE UPDATE (XO. YO.NTHIS.NEUXO.NEUYO)

INTEGER XG (20) , YO (20)

XO(NTHIS) =NELJXO

YO(NTHIS) =NEIJYQ

RETURN

END

SUBROUTINE REPORT( ITIME. NTHIS. IHANDI. INT. IBYSTD, IEVAL.

1 XO, YO, IElAR.TOTBAR.XT, YT.NAGREE.XE, YE. I AGREE.

2 I RAND , P , MOVE . XK . YK . K . NUMEXT. N'UMOCC , TOT I ME . INTLIM,

3 LBYSTD, KNOL'AY, PTD 1ST, TD 1ST, PED 1ST. ED 1ST, NEIJXQ, NEIJYO.

4 EVLOPT, I DOOR, IDOPEN.ND. INTR)

DIMENSION IBAR (20, 20, 2) , INTL IMC20) , LBYSTD (20) . IHAND

1 (20) .• KN0LIAY(20) , IBYSTD(20) ,P(9) , INTR(20)

DIMENSION IDOOR(30,4) , IDOPEN(30,3O)

I NTEGER XE (10), YE ( 1 0 ) , XO (20 ) , YO ( 20 ) . XT, YT, TOT I ME

1 XK. YK, EVLOPT

IF ((ITIME. GT. 1) .OR. (NTHIS. GT. D) GO TO 1

: ECHO: CHECK INPUT PARAMETERS*

: (1) ENVIRONMENTAL:

WRITE f' 100)

C-13
u

uu

uu

a

<_>

—<

<_>

a

u

u

ljcjcmcjcj

URITE 101)

URITE <.w.l02)

URITE (6.104)

URITE (6.105)

URITE (6, 106)

XT, YT

NUMEXT

( XE ( I ) . I = 1 . NUMEXT)

( YE ( I). 1=1, NUMEXT)

: (2) SYSTEM:

URITE (6.109) NUMOCC

URITE (6,116) TOTIMZ

URITE (6,111) I RAND

: (3) OCCUPANT:

URITE (6, 112)

URITE (6,113)

URITE (6,114)

URITE (6,115)

URITE (6,116)

URITE (6.117)

(INTLIM(I). 1=1, NUMOCC)

( LBYSTD (I). 1=1, NUMOCC

(IHANDI (I), 1=1, NUMOCC)

(KNOUAYC I), 1*1. NUMOCC)

CONTINUE

IF (NTHIS.NE. 1) GO TO 2

: IF NTH IS = 1 , PRINT CURRENT TIME MARKER AND COLUMN HEADINGS:

URITE (6.1 IS)

URITE (6.119) I TIME

URITE (6, 118)

URITE (6,120)

URITE (6, 1 IS)

IF ( I NTE ( NTH IS) . EQ . 1 ) GO TO 22

: URITE OUTPUT MATRIX:

IF (EVLOPT-1) 20.20,21

20 CONTINUE

URITE (6.121) NTH IS, XO (NTH IS) , YO(NTHIS) . INT, IBYSTD(NTHIS) . I AGREE,

1 TD I ST, ED I ST, (P (IQ ,K = 1 , 9) .NELJXO.NEUYO

GO TO 23

21 URITE (6.123) NTHIS.XO(NTHIS) ,YO(NTHIS) , INT. IBYSTD(NTHIS)

1 I AGREE, CPCIO ,K=1,9) . NEUXO.NEUYO

GO TO 23

22 URITE (6,127) NTH IS, XO (NTH IS) ,YO (NTH IS) . INT.

1 NEUXO.NEUYO

23 CONTINUE

IF (NTHIS.EQ. NUMOCC) GO TO 3

GO TO 4

3 URITE (6,118)

4 IF ((ITIME.EQ.TOTIME) .AND. (NTHIS.EQ. NUMOCC)) GO TO 5

GO TO 6

5 URITE (6.118)

014
uuu

WRITE (6,124)

WRITE (6,116)

WRITE (6,125)

WRITE (6,118)

DO 30 I = 1 . ND

WRITE (6,126) I , I DOOR ( 1,1), I DOOR ( I , 2) , ID00R( 1,3)

1 (IDOPENd, ITM), ITM-l.TOTIME)

30 CONTINUE

WRITE (6,116)

WRITE (6.122)

WRITE (6.116)

GO TO 6

: OUTPUT FORMATING:

180

101

102

103

104

105

106

10?

103

109

110

111

1 12

1 13

114

115

! 16

1 1 ?

1 10

119

120

121

122

123

124

125

126

12 ?

FORMAT (IX, 120 ('*'), //,55X, ' ECHO-CHECK INPUT PARAMETERS' ,//, 120

1 ('*'),//, IX,' (1) ENVIRONMENTAL:',/)

FORMAT (24X, 'THREATENED EXIT: X= '

. I2.4X, ' Y=' . 12)

FORMAT (24X, ' NUMBER OF EXITS: “ '.12)

FORMAT (24X, 'NO. OF BARRIER PTS=',I3./)

FORMAT (24X, 'COORDINATES OF EXITS: 1 2 3 4 5 6 7 8 9 10'.

1 /:• FORMAT (43X, ' X: ',10(12, IX))

FORMAT (43X,'Y: '. 10 ( 12. IX) . /)

FORMAT (IX. 'BARRIER-POINT MATRIX:',/)

FORMAT (2X,'X:', 33(12. IX) . /. 2X, ' Y: ' , 33 ( 12. IX),/)

FORMAT (IX,' (2) SYSTEM' ,/,24X, 'NUMBER OF OCCUPANTS IN TNE SPACE «'

1 , 13)

FORMAT (24X, 'TOTAL NO. OF TIME INCREMENTS =*',I3)

FORMAT (24X, 'RANDOM NUMBER STARTER ='.I3,/)

FORMAT ( IX, ' (3) OCCUPANT: ' , /, 12X, ' PARAMETER' . 5X, ' OCC NO 1 2 3

14 5 6 ? 8 9 10 11 12 13 1 4 15 16 1? 18 19 20'./)

FORMAT ( 12X, ' INTLIM' , 15X.20C !2, IX))

FORMAT ( 12X, ' LBYSTD' , 15X, 20(12, IX))

FORMAT ( 12X, ' I HAND I' , 15X,20( 12, IX))

FORMAT ( 12X, 'KNO'JAY' , 15X.20C 12, IX) ,/)

FORMAT (2 ( IX. 120 (

’ *' ) ,/)

FORMAT (IX. 120 ('*'),/)

FORMAT (IX, 'TIME = ',13,/)

FORMAT (SX, 'PRIOR' , 18X, 'EXIT' ,89X, 'NEW' ,/, IX, 'OCC' ,2X, 'LOCAT' , 17X.

1' AGREED' ,87X, 'LOCAT' ,/, IX. 'NUM* ,2X, 'XO YO INT IBYSTD UPON

2 F’TDIST TD 1ST PEDIST EDIST P(l) P(2) P(3) P(4) P(5) P(6)

3 P(7) P(S) P(9)',6X,'X0 YO',/)

FORMAT (IX, 12. 3X, 12, IX. I2.4X. 1 1.6X, 1 1.6X. I2,3X.2(7X.F6.3.2X)

1 9CF5.3, IX), 4X, 12, IX, 12)

FORMAT (50X, 'END OF SIMULATION',/)

FORMAT (IX, 12, 3X. 12, IX, I2.4X, I 1.6X, I1.6X.

1 I2,3ZX,9(F5.3. IX). 4X, 12, IX. 12)

FORMAT (SOX, 'DOOR STATUS SUMMARY',/)

FORMAT ( IX, ' DOOR' , 4X, ' X Y' , 5X, ' TYPE' , 5X, ' T» 12345

1 6 7 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

2 24 25 26 27 28 29 30',/)

FORMAT (2X, 12, 5X, 12, IX, I2.7X, I 1,8X.30( 12, IX))

FORMAT ’X, I2.3X, I2.1X, I2.4X, I1.106X, 12, IX, 12)

015
6 RETURN

END

C

C

c SUBROUTINE HBIAS (P)

DIMENSION P(9)

DO 10 101,9

PCKO0.O

10 CONTINUE

RETURN

END

C

C

C

SUBROUTINE EVAL20 (MXTIME.MK,XO, YO,XE, YE,NTHIS, 1AGREE.

1 ITIME,C, IEVAL)

INTEGER XO(20) , YO(20) ,XE( 10) »YE( 10) »XT, YT

mxt=nxti me/mx

IF (IAGREE.EQ.0) GO TO 5

TEST=SQPT(FLOATC (XO (NTH IS) -XE( IAGREE) ) **2+

1 (Y0(NTHIS)-YE(IAGREE))**2))

5 CONTINUE

IF ( (XT.GT.0) .AND. (YT.GT.0) ) GO TO 10

IF ( IAGREE.GT.0) GO TO 20

GO TO 50

10 IF (1AGREE.GT.0) GO TO 20

GO TO 30

C: ESCARE-FENALTY:

20 IF ( ITIME.LE.MXT) GO TO 21

T I ME - 1 T I ME

XTIME=MXTIMI

TD I ST=XT I ME-T I ME/C

GO TO 22

21 XD I ST=XT I ME- ( FLOAT ( MXT)

TD 1ST =XD 1ST

22 IF (TEST. LE.TD 1ST) GO TO 51

GO TO 50

C: THREAT-EVASION PENALTY:

3G QTEST=SQRT (FLOAT ( (X0(NTHIS)-XT)**2+

1 (Y0(NTHIS)-YT)**2))

IF (ITIME.LE.MXT) GO TO 31

T I ME = I T I ME

TDIST=TIME/C

GO TO 32

31 TDIST=0.0

32 IF (OTEST. GE . TD 1ST) GO TO 51

50 IEVAL=0

RETURN

51 IEVAL=

RETURN

END

C

C

C-16
non

C

SUBROUTINE EVAL8 (XO, Y0,XT, YT,XE, YE.NTHIS,

1 I AGREE, I T I ME , IEVAL.PTDIST,TDIST,PEDIST.

1 EDIST, IS, IGOALX, IGOALY)

INTEGER XO(2O),VO(20),XE( 10) , YE C 10) , XT, YT

D IMENG ION PTD 1ST (20) , IGOALX (20, 10) , IGOALY(20, 10)

DIMENSION RED ISTC20)

IF ( (XT.GT.0) .AND. (YT.GT.0) ) GO TO 1 IF ( IAGREE.GT.0) GO TO 2

GO TO 6

1 TDIST=SQRT(FL0AT((X0(NTHIS)-XT)**2+

1 (Y0(NTHIS)-YT)**2))

IF (ITIME.G7.1) GO TO 50

PTD I ST (NTH IS) =TDIST

50 TCHANG=TD IST-PTD 1ST (NTH IS)

IF (IAGREE.GT.0) GO TO 2

IF (TCHANG.GE.0.) GO TO 5

GO TO 6

2 ED IST=SQR7 ( FLOAT ( (XG (NTH IS) - IGOALX ( IS, I AGREE)) **'2

1 +( VO (NTH IS) - IGOALY( IS, I AGREE) ) **2)

IF (ITIME.GT.l) GO TO 55

PED 1ST i NTH IS) =ED 1ST

55 ECHANG-ED IBT-F’ED 1ST (NTH IS)

IF ((XT.GT.0) .AND. (YT.GT.0)) GO TO 3

IF (ECKANG.LE.0. ) GO TO 5

GO TO G

3 IF ( (TCHANG.GE.6. ) .AND. (ECHANG.LE.0. ) ) GO TO 5

GO TO 6

5 I EVAL = 1 GO TO ?

6 IEVALO

7 PTD IS"!

- (NTH IS) =TDIST

PED 1ST (NTH IS) =ED 1ST

RETURN

END

SUBROUTINE JAMMED ( ITIME, NTH IS, IHAND I , INT, IBYSTD, 1EVAL,

1 XO.YO, IBAR, TOTBAR,XT, YT,NAGREE,XE, YE, I AGREE, IRAND,

2 P, MOVE, XK, YK, K, I ALLOW, NUMOCC, I ENTER)

INTEGER XO ( 20 ) , YO ( 20 ) , XK . YK , XE , YE

DIMENSION JAM(S) , IENTER (9)

DO 100 K = 1 ,

CALL KTOXY ( I TIME, NTH IS, I HAND I , INI, IBYSTD, IEVAL,

1 XO, YO, IBAR, TOTBAR, XT, YT, NAGREE, XE, YE, I AGREE, IRAND,

2 P, MOVE, XK, YK, t<) JAM(K) =0

DO 200 N=l, NUMOCC

IF (N . EQ . NTH IS) G3 TO 200

IF ( (XO(N) .EQ.XK) .AND. (YO(N) .EQ.YK)) GO TO 1 GO TO 200

1 JAM(K) =JAM(IO + 1 200 CONTIN

C-17
n

n

n

£3,000

ro

oj

cn

n

n

n

non

IF (O'

1 “Q.XE) .AND. (YK.EQ.YE)> GO TO 2

IF (Jfl, X) .LT. I ALLOW) GO TO 2

GO TO 3

2 IENTER(K) =1

GO TO 100

3 IENTERCIO =0

160 CONTINUE

RETURN

END

SUBROUTINE D00RS1 ( ITIME. NTH IS. IHAND I . INT, IBYSTD. IEVRL.

1 XO.YO, IBAR.TOTBAR, XT, YT, NAGREE, XE, YE, 1 AGREE,

2 IRAND,P,MOVE,XK,YK.K,L, IS, IGOALX, IGOALY, I ENTER,

3 X, IDOOR, F'OFEN, ND. MDCOR, PCLuSE, IX)

DIMENSION P(9), IDOOR (30, 4) , POREN (20)

INTEGER XC (20) , YO (20) , XK, YK. TOTBAR

: DETERMINE WHETHER OCC(I) ENCOUNTERS A DOOR:

K"0

ic=:<+i CALL KTOMY ( ITIME, NTH IS, I HAND I,' INT, IBYSTD, IEVAL,

1 XO,YO, I BAR , TOTBAR , XT, YT, NAGREE , XE , YE , I AGREE

2 I RAND , P , MQV'E , XX , YK , !<

I CROSS = ( XO ( NTH I S ) +XK ) /2

JCROSS = ( YO ( NTH I S ) +YI< ) /2

1=0

J =0

J=1

1 = 1 + 1 IF (I.GT.ND) GO TO 1 IF ( I DOOR ( I, J) .EG. I CROSS) GO TO 2

GO TO 3

J=J+1

IF ( IDOOR ( I, J?.EQ. JCROSS) GO TO 4

J=J-1

GO TO 3

MDOGR=C

IF (K.EQ.9) GO TO 939

GO TO 5

: DOOR IS ENCOUNTERED:

IX=I

MDOOR=K

: IF DOOR IS ALREADY OPEN, RETURN:

IF ( IDOOR ( 1,4) .EQ. 1) GO TO 999

CALL RANDOM ( ITIME. NTH IS, IHANDI , INT. IBYSTD. IEVAL,

1 XO , YO , IBAR, TOTBAR.XT, YT, NAGREE, XE. YE. IAGREE.

2 I RAND , P , MOVE , XK , YI< . K , L . IS, IGOALX. IGOALY. I ENTER. X)

C-18
IF (X) 55.56.5S

55 X=X>:'(-1)

56 IF (X. LT. POPEN (NTH IS) ) GO TO 6

C

C: OCC(I) OPENS CLOSED DOOR:

C

I DOOR ( 1.4) =1

RETURN

GO TO 99?

C

C: OCC(I) LEAVES DOOR CLOSED:

C

6 P(MDO0R) =0.0

C

C: OCC •: I ) CHOSES TO LEAVE DOOR CLOSED:

C: REDISTRIBUTE MOVE PROBABILITIES:

C

K=0

si jr 1 = 0.0

NP0SS=0

7 K=l(-i-l IF (IC.GT.9) GO TO 9

CALL KPOSS ( ITIME.NTHIS. IHANDI. INT. IBYSTD. IEVAL.

1 XO.VO. I BAR , TOTBAE , XT. VT. NRGREE . XE , YE . I AGREE.

2 I RAND. P. MOVE . XK. YK.K.L, IS. IGOALX. IGOALY. I ENTER)

IF (L.EO. 1) GO TO 8

GO TO 7

6 NF0SS=NP0SS+1

SUII=SUM+PCIO

GO TO 7

9 DIFF=1. O-SUM

SNARE =D IFF/FLOAT (NPOSS)

DO 25 K = 1 .

CT.LL KF'OSS (ITIME.NTHIS. IHANDI, INT. IBYSTD, IEVAL.

1 XO.VO. IBAR. TOTBAR. XT. YT.NAGREE. XE. YE. I AGREE.

2 I RAND . P . MOVE . XL .YK.K.L. IS. IGOALX, IGOALY. I ENTER)

IF (L.EO. 1) GO TO 10

GO TO 25

10 P CIO =P CIO +SKARE

25 CONTINUE

999 RETURN

END

C

c

c SUBROUTINE D00RS2 ( ITIME.NTHIS, IHANDI, INT. IBYSTD. IEVAL.

1 XO.YO, I BAR , TGTBAR , XT, YT, NAGREE , XE, YE , I AGREE.

2 IPAND.P, MOVE, XK, YK.K.L. IS, IGOALX, IGOALY, I ENTER.

3 X, I DOOR, POPEN, ND.MDOOR.PCLOSE, IX)

DIMENSION I DOOR (30, 4) ,PCLOSE(20)

C

C: DETERMINE WHETHER OCC(I) CLOSES (A MANUAL) DOOR BEHIND HIM:

C

IF (MD 1 > .EQ.0) GO TO 999

C-19
IF (MO' EQ.MDOOR) GO TO 1 GO TO „_J

1 IF ( I DOOR ( IX, 3) . EQ . 0) GO TO 2

GO TO 999

2 CALL RANDOM < ITIME.NTHIS. IHANDI. INT, IBYSTD. IEVAL.

1 XO.YO. IBAR.TOTBAR.XT, YT.N AGREE. XE, YE. IAGREE.

2 IRAND. P. MOVE, XK, YK.K.L. IS. IGOALX. IGOALY.

3 I ENTER. X)

IF (X) 55.56.56

55 X=X*<-1>

56 IF (X. LT. PCLOSE (NTH IS) ) GO TO 999

IDOORC IX. 4) =0

9S9 RETURN

END

C

C

C SUBROUTINE INTRPT C I TIME. NTH IS. I HAND I . INT, IBYSTD. IEVAL.

1 XO.YO. IBAR. TOTBAR.XT, YT, NAGREE, XE. YE. IAGREE.

2 IRAND.P.MOVE.XK. YK.K.L, IS. IGOALX. IGOALY. I ENTER.

3 X. INTLIM. INTR. INTNUM.PI2.PI0)

DIMENSION I NTNUM (20) , INTLIMC20)

DIMENSION INTR (20)

IF ( INTR (NTH IS). EC!. 1) GO TO 1 IF ( I NTNUM ( NTH IS) . L.E . I NTL I N ( NTH I S ) ) GO TO 2

INT=0

RETURN

1 INT =2

RETURN

2 CALL RANDOM ( ITIME.NTHIS, IHANDI. INT, IBYSTD. IEVAL.

1 XO.YO. IBAR.TOTBAR.XT, YT.KAGREE.XE. YE, IAGREE,

2 I RAND , P , MOVE , Xl< .YK.K.L, IS. IGOALX. IGOALY, I ENTER.

3 X)

IF (X> 5.6.6

5 X=X>:-C-1)

6 CONTINUE

IF (X. LT. P 12) GO TO 3

IF (X.LT.PI0) GO TO 4

I NT = I

I NTNUM (NTH IS) = INTNUM(N'THIS) + 1 RETURN

3 I NTNUM (NTH IS) = INTNUM(NTHIS)+1

INTR (NTH IS) = 1 INT =2

RETURN

4 INT =0

RETURN

END

C

C

c SUBROUTINE BACKUP ( IBACK, XO.YO, INITXO, INITYO.XOB. YOB,

1 I T I ME , NTH I S , NEUXO , NEUYO , INTR, JT I ME)

DIMENSION I BACK ( 20 ) , INI TXO ( 20 )

C-20
1

INITYOC20) » jrTIME(20) , INTR(20)

INTEGER XOB(20,30),YOB(20,30),XO(20),YO<20)

IF ( IBACK(NTNIS) . EQ.0) GO TO 1 IF ( (XO(NTHIS) .EQ. INITXQ(NTHIS) ) . AND.

1

(YO(NTHIS) .EQ. INITYO<NTNIS) ) ) GO TO 3

1 IBACK(NTHIS) IBACK(NTHIS)+1

IF ( IBACK(NTHIS) .EQ. 1) GO TO 2

JTirt (NThIS) -JTIME (NTHIS)-l

GO TO 4

2 IF (ITIfE.GT.l) GO TO 21

JTIhE(NTHIS)-l

GO TO 4

21 JTIME (NTH IS) "1TIME-1

4 KTIME*JTIME(NTHIS)

NEUXQ-X0B(NTHIS,KT1ME)

NEUYO«YOB(NTHIS,KTIME)

RETURN

3 INTR(NTHIS) *0

1 BACK (NTH IS) “0

RETURN

END

C

C

C

SUBROUTINE TRACE ( IXTRCE, IYTRCE,NTHIS, ITIME.NUMOCC,

1 TOTIME)

DIMENSION IXTRCE(20,30),IYTRCE(20,30)

INTEGER TOTIME

URITE (6,1)

DO 25 I-l.NUMOCC

URITE (6,2) I DO 24 J-l, TOTIME

URITE (6,3) J,IXTRCE(I,J),IYTRCE(I,J)

24 CONTINUE

URITE (6,4)

25 CONTINUE

URITE (6,5)

1 FORMAT (IX, 120('*').//,48X,

1 'OCCUPANT MOVEMENT TRACES',//, IX, 120('*'))

2 FORMAT ( IX, 120('*' ) ,//, IX, 'OCCUPANT NUMBER:' , 14,/,

1 10X, 'TIME' ,/, 10X, ' FRAME ' , 1 0X, ' X' , 5X, ' Y' , /,

2 IX, 120 ('*'))

3 FORMAT ( 12X, 12, 10X, 12, 4X, 12)

4 FORMAT (IX, 120('*'))

5 FORMAT ( IX, 120 ('*'), //, 55X, ' END OF TRACES',//,

1 IX, 120('*'))

RETURN

END

C

c

c SUBROUTINE PASSG (IDPASS, IDOOR,XO, YO,NTHIS.

1 ND,NEUXO,NEUYO)

DIMENS' « 1DPASS(20). IDOOR(30,4) i

C-21
non

oo

n

cn

oi

tiww

M

>-*

i\j

oo

o

INTEGEr '0 (20) . Y0 (20)

IXPhSS ,<EWX0+X0(NTHIS))/2

I YFASS=( NEWYO+YG ( NTH I S ) ) /2

DO 10 1=1. ND

IF ( ( ID00R ( I . D . EG . IXPASS) . AND

1 ( I DOOR ( I . 2 ) . EQ . I YPASS ) ) GO TO 5

GO TO 10

5 IDPASS (NTH IS) c IDPASS (NTH IS) + 1 10 CONTINUE

RETURN

END

SUBROUTINE TOTALS ( IDPASS. 1 TYPE 1. 1 TYPE2, NTH IS, NUMOCC)

DIMENSION IDPASS (20) . ITYPE1 (20) , ITYPE2(20)

DIMENSION IOCC (20)

DO 25 I=1.NUM0CC

5 IOOC C I :< *= I IJRITE (6.1)

WRITE (6,2) ( IOCC ( I ) . I=1,NUM0CC)

WRITE (6,3) (ITYPE1 (NTHIS), NTH IS- 1. NUMOCC)

WRITE (6,31) ( ITYPE2 (NTHIS) , NTH IS=i . NUMOCC)

WRITE (6,4)

WRITE (6.5) (IOCC (I), 1=1. NUMOCC)

WRITE (6,6) ( IDPASS (NTHIS) . NTH IS = 1 . NUMOCC)

WRITE (6,?)

WRITE (6,8)

FORMAT (IX, !20('*').//.45X,

1 'OCCUPANTS INTERRUPTION TOTALS : ' . 4/, 3SX.

2 'REPORTS TOTAL NUMBER OF FRAMES SPENT IN MODES'.

3 IX, 120 ('>::'))

FORMAT (IX,' INTR?T’,28X.' OCCUPANT'. /,2X,' TYPE'.

1 iOX. CO ( 13)

FORMAT (IX, 1201'*’), /.4X,' 1', 11X, 20(13))

1 FORMAT (4X,'2', 1 IX, 20 (13),/, IX, 120 ('*'))

FORMAT (IX, 120('*'),//,5OX, 'DOOR PASSAGE TOTALS'.

1 IX, 120 (' *' ) ,/,55X, 'OCCUPANT')

FORMAT (30X, 20(13),/, IX, 120 ('*’))

FORMAT (30X,2O( 13) IX, 120 ('«'))

FuRMAT (IX, 120('»M),//,52X,'END OF TOTALS' .//, IX.

1 120 ('>!•' )

FORMAT (IX. 120 ('*'),//, 55X,' END OF RUN' .//. IX. 120C '*' )

RETURN

END

SUBROUTINE NEWXY ( ITIME.NTHIS, I HAND I, I NT, IBYSTD. IEVAL.

1 XO,YO. IBAR, TOTBAR. XT, YT, NAGREE, XE, YE. IAGREE,

2 I RAND , P , MO VE , XK , YK , l< , NEWXO , NEWYO

INTEGER XK. YK

K=MOVE

CALL KTOXY ( ITIME.NTHIS.HAND I, I NT. IBYSTD. IEVAL,

C-22
1 XO.YO, I BAR. TDT8AR.XT* YT, NAGREE. XE. YE. S AGREE.

2 IRAND.P.flOVE.XK. YK.K)

neu:-;ci=>:k NElJYCl=YK

RETURN

END

C-23

APPENDIX D: PREPARING A BFIRES INPUT FILE

This Appendix provides the user with basic information needed to run

a simulated fire event with BFIRES. As described elsewhere in the report,

the user must provide four basic types of input data. These are:

( 1 ) data which define or describe the layout of the building floor

under study;

( 2 ) data which define or describe initial environmental conditions;

(3) data which describe individual differences among occupants in

the simulated fire event; and

(4) data which initialize system functions.

Two tasks are required in the preparation of a BFIRES input file.

These are the determination or selection of actual numerical values which

describe the simulated event, and the organization of these data into the

appropriate machine-readible format. Value selection is described below.

Input format requirements are described in Table D.l, and a sample data

file is provided in Table D.2. A glossary if input variable names is

given in Table D.3.

D. 1 Floor-plan Description

Certain data must be input to describe the desired floor-plan,

as a whole (refer to Figure D.l). These are:

(1) the number of exits from the floor (NUMEXT);

(2) the x,y coordinates of each exit (XE[e] and YE[e]);

(3) the total number of rectangular spaces (e.g., rooms) comprising the floor (NSPACE);

(4) the location of doors (IDOOR| gives x coordinates, IDOOR

2 gives y coordinates);

(5) door type, i.e. , manually versus automatically closing

(IDOOR

3

); and

( 6 ) the total number of doors on the floorplan (ND).

Other descriptive data will relate specifically to a given space

within the floor-plan, and these data must be read-in on a space-byspace basis (refer to Figure D.l):

D-l
( 1 )

1

the total number of exits from the space (NE);

(2) the total number of wall-defining coordinate points required

to enclose the space (NPOINT);

(3) x,y coordinates of wall-defining points (IBARj gives x coordinates, IBAR

2 gives y coordinates);

(4) exit-goal points outside each exit from the space (IGOALX (e)

gives x coordinates, IGOALY (e) gives y coordinates);

(5) overall x boundaries of the space (XLO gives lowest x value,

XHI gives highest x value);

(6) overall y boundaries of the space (YLO gives lowest y value,

YHI gives highest y value).

D.2 Initial Environmental Conditions

Two initial conditions are provided. These are:

(1) the x,y coordinates of the threatened exit (XT and YT); and

(2) the status of each door on the floor-plan, i.e., whether it

is initially open or closed (IDOOR^).

D. 3 Occupant Description

The specification of seven conditions are required to describe each

occupant in the simulation:

(1) interruption limit, i.e., the maximum number of interruptions

the occupant will respond to (INTLIM);

(2) bystander intervention limit, i.e. , the maximum number of

interventions an occupant will engage in (LBYSTD);

(3) the occupant's mobility status (IHANDI);

(4) the occupant's familiarity with an exit from the floor (KNOWAY)

(5) the occupant's location on the floor (XO gives the x-coordinate

YO gives the y-coordinate) ; refer to Figure D.2;

(6) the probability that the occupant will open a closed door

(POPEN); and

(7) the probability that the occupant will close an open door

passed through (PCLOSE).

D-2
D. 4 System Initialization

Before a simulation run may be conducted, several additional

parameters must be initialized. These include:

(1) the maximum number of occupants permited in a single spatial

location at any given time (IALLOW);

(2) the desired print-out option (REPORT);

(3) the desired status evaluation option (EVLOPT); see default

values below;

(4) the total number of occupants in the simulation (NUMOCC);

(5) the total number of time-frames to be run (TOTIME);

(6) the random number seed (IRAND);

(7) the total number of replications of a given simulation

desired (NUMREP);

(8) the probability of encountering a backtracking interruption

(PI2);

(9) the probability of encountering no interruptions (PIO).

For the current version of BFIRES, the following default values must be

used

(1) EVLOPT = 1

(2) MXTIME = 0

(3) MK = 0

(4) C = 0

D-3
TABLE

D.l.

Input

Data

Sequence

and

Format

x

CN2X! 1 CNO

i—

Ph' XX

<r <r CJ < C < 2 O O O lm

CM tM CM

m

Hfr 2#> X

CM

CMMOs X2

LA ,—

,—

<3 CO f—

i—l Ml *1 H 2 rM i—1 CO i—i i—

d X <3 CO cO £ d c £ O 4J 3 3 O a) CO cC 2 X cO M Mi 2 2 CO w c CO CO CM a) CL CL

W

X oo 2 Mi Ml 0) C/0 * •H d C CO Ml O O 2 o Q CO o O M O O Ml 2 2 0) 2 CO 2 i—1 u

T3 00 00

X CO 2 2 d Ph 2 d C CD •>> O 01 •H 2 CO <3 <3 z 2 CJ X X 0) — 2 2 2 w 2 co i—i 2 U 03 Ml Ml d Ph

<d CL cO Ml co o M co CO CO O c d a Ph

O' 2 Ml QJ

O o o ,—

r

—

d

CJ 2 c C

U o d

d o O

r •X o •H 2 CD CO co CO co CJ

X 2 K CO O O 2 X Mi Mi 2 2 o #» £ N 01 CL a mi <3 (3 Cl CL o H C X Ml Ml Ml Mi TSl 2 X H 'w' Ci •d d co d d d O O 2 o

PhW o •H CO CO 0) C3OO Ph CO

X

O >-« CM

M Ml £ uO 2 2 <3 < 2 * oo 00 CO 2 2 CN > CD - CO Mi Mi 2 Mi o w CM O ,—

r-l

d co r—o O a> <3 Ph

O

C i—

2 Ml ,—<3 o O 2 2 •» 2 § w w a) cO co O O 2 d Ml Ml cu •> o >- 3 o X CO Jjj (3 2 < cr CM CM CJ 2 CL i—

2 d d 2 d Q

Ph 0) o o < CO 2 2 o o § •H £

CO 1—

CO

x

Q CO CO 2 X 2 vw' CO CO 3 (3 Mi Ml x w oo — - ^ M 2 2 2 2 O O oo 2

pj >-« c T3

d c Ml pc

O o o o cTd •> 2 •H Pi Ml co r X 2 2 •H Q M /*N 3 . 

o O £ 1—

£ CO CO 3 3 H 2 c o d o O a> O o — - 2 2 o CP X V—

r-H CO

H o CJ CO o Mi 2 2 d <J CJ

M I—1 X 2 w »—

r—

2 co cO CMX Mi Mi CO co rH 2 X o CL i—

X X^ CL O O (3 <3 CxD o 2 H O' 2 l-l O 1—

•—

CO CO h—

O o § 2 X 2-N o PH X Ml 2 cj CJ Mi Mi M S

pj CM 0) o 2 rH CNi 1 <3 2 X

O

OH <3 2 w £ 2 2 a PC < 21 X X X 2 2 O 2 2 2 w 2 CM o. < < O Ml (3 o 1

' « * 1 i—

H 2 H

(—

2 X

w PQ PQO 2 Z 2 r O' 2 0) 2 M M M X 2 N co -JO 2 2 * * H cO 2 Pi P

Ph Pi o CO c H H X t—

OJ 2 O o O

O o <3

H S £ *• cl

Q

© o o £ CL < [_L_ H w QJ d S Q Q a (L) Q M X X X X O 2 M 2 2 z Pi

D-4

Notes: *

user-supplied

format

statements

**

up

to

NUMEXT,

the

total

number

of

exits

from

the

floor
TABLE D.2. Sample Input File

TYPE OF INPUT DATA ACTUAL INPUT FILE

(1) User-supplied format statements (20(12, IX))

( 20 ( 12 ))

(3(11, IX). 3(12# IX) ,2(F3i2. IX))

(2) Threat, exit, and system data

(3) Space-defining data: Space #1

Space #2

Space #3

Space #4

Space #5

Space #6

Space #7

Space #8

1

Space #9

02 04 02 05 14

18 02 04 04

01 0?

03 4 5 5 5 3 3

01 1 1 2 3 3 2

04 04 04 04

03 05 01 03

01 07

05 6 7 7 7 5 5

01 1 1 2 3 3 2

06 06 04 04

05 07 03 03

01 07

0? 8 9 9 9 7 7

01 1 3 2 3 3 2

08 08 04 04

0? 09 01 03

01 07

0910111311 9 9

01 1 1 2 3 3 2

10 10 04 04

09 1 1 03 03

01 07

1 1 1213131311 1

01 1 1 2 3 3 2

12 12 04 04

1 1 13 01 03

03 07

13141515151313

01 1 1 2 3 3 2

14 34 04 04

13 15 01 03

01 07

15]161717171515

01 1 1 2 3 3 2

lb 16 04 04

15 17 01 03

01 07

03 5 5 5 4 3 3

05 5 6 7 7 7 6

0~t 04 04 04

03 05 05 07

01 07

05 7 7 7 6 5 5

05 5 6 7 7 7 6

00 06 04 04

U .

• 07 05 87

D-5
TABLE D. 2 , continued

(3) Space-defining data, continued:

Space #10

Space #11

Space #12

Space #13

Space #14

(4) Door-defining data

(5) System data

(6) Occupant data

61 07

07 9 9 9 8 7

65 5 6 7 7 7

68 06 04 04

07 09 05 07

01 07

0911111110 9 9

0-5 5 6 7 7 7 6

10 10 04 04

09 11 05 07

01 15

111213151515151514131211111111

05 55567899999676

14 14 04 04

11 15 05 09

01 15

151718191919191918171615151515

05 5 5 5 6 7 8 9 9 9 9 9 8 7 6

16 16 04 04

15 19 05 09

02 17

03 5 7 9111315171715131211 9 7

03 3 3 3 3 3 3 3 5 5 5 5 5 5 5

16 02 04 04

03 17 63 05

03 4 6 8 1012141617161410 8 6 4

04 3 3 3 3 3 3 3 4 5 5 5 5 5 5

00 0 0 0 0 0 0 0 0 0 0 0 0 0 0

00 0 0 0 0 0 0 0 0 0 0 0 0 0 0

88 30 08895 05 0 , 10 0,80

3 3 1 01 14 02 ,25 ,25

3 3 0 01 14 02 ,75 ,75

3 3 1 01 14 02 ,25 ,75

3 3 0 01 14 02 ,75 ,25

5 3 0 61 14 02 ,25 ,25

5 3 1 01 14 02 ,75 ,75

5 3 0 01 14 02 ,25 ,75

5 3 1 01 14 02 ,75 ,25

D-6
TABLE D.3. Glossary of Input Variable Names

LABEL DEFINITION FORMAT

C This parameter must be specified whenever EVAL20 is called F

EVLOPT Evaluation subroutine option selector:

1 = EVAL8 2 = EVAL20 I

IALLOW Maximum number of occupants allowed in a given space

during a single time-frame I

IBAR

1 X coordinate of a wall point I

ibar

2 Y coordinate of a wall point I

I DOORX coordinate of a door center I

IDOOR^ Y coordinate of a door center I

I DOOR Door-type designator for a given door:

0 = manual 1 = automatically-closing I

IDOOR

4

Initial position of a door:

0 - closed 1 = open I

IGOALX X coordinate of an exit goal from a space 1

IGOALY Y coordinate of an exit goal from a space I

IHANDI An occupant's mobility status:

0 = mobile 1 = immobile I

INTLIM An occupant's interruption limit I

I RAND Random number seed [any 5_ digit odd number] I

KNOWAY An occupant's knowledge of a "best exit":

0 = no knowledge

1 = exit #1

2 = exit #2 I

LBYSTD An occupant's bystander intervention limit I

MK This parameter must be specified whenever EVAL20 is called I

MXTIME This parameter must be specified whenever EVAL20 is called I

D-7
TABLE D.3, continued:

LABEL DEFINITION

ND

NE

NPOINT

NSPACE

NUMEXT

NUMOCC

NUMREP

PCLOSE

PI0

PI2

POPEN

REPORT

TOTIME

XE

XHI

XLO

XO

XT

YE

YHI

YLO

YO

YT

Number of doors on the floorplan

Number of exits from a space [max = 2]

Number of wall-defining points for a given space

Number of spaces designated on the floorplan

Number of exits from the floor [max = 2]

Number of persons initially on the floor [max = 20]

Number of replications of a run desired

Probability that an occupant will close an open door

Probability of no interruption encountered during t

Probability of encountering/initiating back-tracking during t

Probability that an occupant will open a closed door

Print-out option selector:

0 = print-out traces and totals only

1 = print-out time-frame reports and

door-status summaries, only

Number of time-frames desired in the rum [max = 30]

X coordinate of an exit from the floor

High X coordinate of a space

Low X coordinate of a space

X coordinate of an occupant's location at time t = 1

X coordinate of the threatened exit

Y coordinate of an exit from the floor

High Y coordinate of a space

Low Y coordinate of a space

Y coordinate of an occupant's location at time t = 1

Y coordinate of the threatened exit

FORMAT

I

I

I

I

I

I

I

F

F

F

F

I

I

I

I

I

I

I

I

I

I

I

I

D—
Legend

Coordinate points which describe wall locations

Coordinate points which locate doors

Coordinates of person-occupiable locations

FIGURE D. 1 Describing the Floor-plan for the Computer, in X,Y Space

D-9
FIGURE D .

V 2.

--- -3 - f- tS r—£ f- —

1 ;

i i ...J

1

-to

- T

I

'

i 43;

2 :

6,2 _6:.i6, 6

A

3 : 8,2

It 10,-4

7; :8,6

STlO,

-j -j_

i : ...1

.4:5 . lOx.2

ft

5: 14,2

! I'

9:1?, 6 10: 14,8

i : ’!

41

-4b

i . I

Legend:

y coordinate

x coordinate

occupant number

Locating Occupants on the Floor-plan

D- 10
FEDERAL INFORMATION PROCESSING STANDARD SOFTWARE SUMMARY

01. Summary date

Yr. Mo. Day

in 0 lJL ILZ

04. Software date

Yr. ] Mo

t 718; oj 7

02. Summary prepared by (Name and Phone)

Fred I. Stahl (301) 921-2627

05. Software title Computer Simulation of Human Behavior in Building

Fires

06. Short title

08. Software type BFI RES

09. Processing mode

03. Summary action New Replacement Deletion E

Previous Internal Software ID

07. Internal Software ID

10. General

Application area

Automated Data

| | System

J~X] Computer Program

I Qj Subroutine/Module

| | Interactive fg Batch Q] Combination

Computer Systems

| | Support/Utility

Scientific/Engineering

Bibliographic/Textual

11. Submitting organization and address

Management/

Business

Process Control Other

Specific Computer simulation of

human behavior

12. Technical contact(s) and phone

1

Architectural Research Program

Environmental Design Research Division

Center for Building Technology

National Bureau of Standards

Washington, DC 20234

Fred I.

13. Narrative

Stahl (301) 921-2627

BFIRES aids the prediction of escape times and escape routes of building

occupants during fires. The program executes a descrete time stochastic

simulation of human movement within bounded physical spaces, in response

to specified fire conditions. The simulation is based on a non-stationary

Markov model of building fire events. Input/output is via punched cards and

line printer, or via terminal.

I

I

14. Keywords

Building fires; computer-assisted building design; fire research; fire safety;

human performance simulation.

15. Computer manufr and model UNIVAC 1108

16. Computer operating system

EXEC 8

17. Programing language(s)

FORTRAN V

18. Number of source program state- ments 1153

19. Computer memory requirements

28.62 K 36-bit

20. Tape drives 21. Disk/ Drum units 22. Terminals

words 1

23. Other operational requirements

~ 24. Software availability 25. Documentation availability

Available * s

Limited In-house only Available

NBSIR 78-1514, NTIS , Springfield

Virginia 22151

Inadequate

a

In-house only

26. FOR SUBMITTING ORGANIZATION USE

Standard Form 185

1974 July

U.S. Dept, of Commerce—NBS

(FIPS. Pub. 30)

185-101
NBS-l 14A (REV. 7-73)

U S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET

1. PUBLIC AT ION OR REPORT NO.

NBSIR 78-1514

2. Gov’t Accession

No.

3. Recipient’s Accession No.

4. TITLE AND SUBTITLE

A Computer Simulation of Human Behavior in Building Fires:

Interim Report

5. Publication Date

6. Performing Organization Code

7. AUTHOR(S)

Fred I. Stahl

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No. 7400504

11. Contract/Grant No.

12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP)

Same as item 9

13. Type of Report & Period

fnFerim Report:

2/77 - 2/78

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here.)

This interim report presents the conceptual development, structure, and function of

BFIRES, a computer program designed to simulate human movement behavior during

building fires. The basic model underlying BFIRES is derived from a non—stationary

,

discrete time Markov Process. This model postulates that occupants construct their

emergency responses and behavioral decisions dynamically, in response to continually

changing social and environmental information fields. The simulation of this

process is accomplished through BFIRES, a computer program written in FORTRAN V.

Directions for further study are discussed.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a propter name; separated by semicolons) Architectural psychology? architectural research; building

fires; computer-aided design; fire computer program; fire research; fire safety;

human performance; model documentation; modeling technique; programming; simulation.

18. AVAILABILITY [~X] Unlimited 19. SECURITY CLASS 21. NO. OF PAGES

(THIS REPORT)

For Official Distribution. Do Not Release to NTIS 131

UNCL ASSIFIED

1 Order From Sup. of Hoc., U.S. Government Printing Office Washington, D.C . 20102. SD Cat. No. Cl 3

20. SECURITY CLASS

(THIS PAGE)

22. Price

X Order f rom National Technical Information Service (NTIS) $7.25 Springfield, Virginia 22151 UNCLASSIFIED

USCOMM-DC 2 904 2- P 7 4
f

* 4

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